1、Design and construction of a pilot plant for the production of aviation kerosene using Fischer Tropsch technology: leading the design of a thousand ton scale Fischer Tropsch pilot plant, reviewing PID, key equipment selection (such as reactors and separation towers), and safety interlock system configuration (HAZOP analysis)。

2、Operation and evaluation of the pilot plant for Fischer Tropsch aviation fuel: leading the operation, evaluation, and system integration optimization of the pilot plant.

3、Economic demonstration of Fischer Tropsch aviation coal technology: Construct a CAPEX/OPEX model for a 10000 ton factory and output an investment feasibility report; Provide quantitative decision-making basis for technology selection of 10000 ton level factories.

4、Cross departmental collaboration: collaborate with the procurement team to optimize the equipment supply chain, and collaborate with the business team to develop product strategies.

1、Education major: Master's degree or above, major in Chemical Engineering, Catalysis, Chemical Machinery, Chemical Reaction Engineering, or related fields.

2、Knowledge system: Proficient in the principles of Fischer Tropsch synthesis and oil hydrogenation technology; Familiar with chemical thermodynamics, reaction engineering, and process design specifications.

3、Analytical skills: Possess technical feasibility assessment, life cycle analysis (LCA), and economic modeling abilities; Rigorous logic, able to propose practical technical decisions based on data.

4、Industry experience: Over 3 years of experience in research and development, pilot or commercial project operation of Fischer Tropsch synthesis, and engineering design experience in Fischer Tropsch synthesis.

Salary and Benefits

Welfare: Five insurances and one fund, national and corporate annual leave, rental subsidies, vehicle subsidies, other benefits, etc.

1、 Technical evaluation and route analysis: Systematically evaluate the efficiency, cost, and applicable scenarios of different carbon capture and utilization (CCS) technology routes to provide a basis for project selection.

2、 Technical and Economic Modeling: Participate in the construction of the Carbon Capture Full Process Cost Model (CAPEX/OPEX) to quantify energy consumption, material loss, and economic benefits.

3、 Innovation technology tracking: Monitor international cutting-edge CCS technologies (such as new carbon capture absorbents, efficient adsorption materials, carbon capture utilization integrated technologies, etc.), analyze their commercialization potential and intellectual property layout.

4、 Policy and Market Research: Interpreting Domestic and Foreign Carbon Neutrality Policies, Evaluating the Impact of Policies on Technological Route Selection.

1、 Education major: Master's degree or above, major in Chemical Engineering, Environmental Engineering, Materials Science or related fields, PhD preferred.

2、 Knowledge system: Proficient in the principles of carbon capture technology; Familiar with chemical thermodynamics, reaction engineering, and process design specifications.

3、 Analytical skills: Possess technical feasibility assessment, life cycle analysis (LCA), and economic modeling abilities; Rigorous logic, able to propose practical technical decisions based on data.

4、 Industry experience: Over 3 years of experience in carbon capture technology research and development, engineering design, or consulting analysis.

Salary and Benefits

Welfare: Five insurances and one fund, national and corporate annual leave, rental subsidies, vehicle subsidies, other benefits, etc.

1、 Fischer Tropsch synthesis test operation and evaluation: responsible for the opening, daily operation, process monitoring, parking, and maintenance of small-scale or pilot scale Fischer Tropsch synthesis test devices. Strictly implement the experimental plan, conduct experiments on catalyst loading, reduction, activation, reaction performance evaluation, and deactivation regeneration. Accurately control and record key process parameters such as reaction temperature, pressure, feed flow rate (synthesis gas), and exhaust emissions to ensure safe, stable, and long-term operation of the experiment. Systematically investigate and analyze the effects of different process conditions on catalyst activity, selectivity, and stability, including conversion rate, product distribution (CH ₄, CO ₂, light hydrocarbons, oil products, heavy wax, etc.), and lifespan.

2、 Sample collection and instrument analysis: Responsible for the standardized and timely collection and pretreatment of gases, liquids (oil, water), and solid wax products during the Fischer Tropsch synthesis reaction process. Independently operate and maintain various analytical instruments to perform qualitative and quantitative analysis of products, including online and offline gas chromatography: analyze the composition of H ₂, CO, CO ₂, CH ₄, and C1 to C5 light hydrocarbons in exhaust gas. Gas chromatography-mass spectrometry: used for detailed analysis of complex organic compounds (olefins, alkanes, oxygen-containing compounds, etc.) in oil and water phases. Simulated Distillation Gas Chromatography: Rapid Determination of Distillation Range Distribution of Liquid Products. Other related analyses: such as using total sulfur/total nitrogen analyzer, Karl Fischer moisture analyzer, etc. to analyze impurities in raw materials and products. Ensure the accuracy, reliability, and traceability of the analyzed data.

3、 Data processing and report writing: Summarize, calculate, verify, and statistically analyze the raw experimental data and instrument analysis results, calculate key performance indicators (such as CO conversion rate, hydrocarbon selectivity, ASF distribution, etc.). Write clear and detailed daily experiment records, test reports, and periodic technical summaries. Utilize data analysis results to assist R&D engineers in analyzing experimental phenomena, revealing reaction patterns, and providing suggestions for the next experimental direction.

4、 Safety and laboratory management: Strictly comply with laboratory safety regulations for high pressure, high temperature, hydrogen and toxic and harmful gases, participate in hazard identification and risk assessment. Responsible for the daily calibration, troubleshooting, and maintenance of the analytical instruments used, maintaining the cleanliness and orderliness of the experimental area and analysis room. Manage experimental samples, chemical reagents, and analytical consumables.

1、 Essential requirements: Major in Chemical Engineering, Industrial Catalysis, Analytical Chemistry, Applied Chemistry, or related fields, with a bachelor's degree or above. Excellent candidates can be extended to associate degrees.

2、 Work experience: Possess experience in independently or mainly participating in the operation of Fischer Tropsch synthesis or similar multiphase catalytic reaction (such as methanol to gasoline, methane reforming, etc.) experimental equipment in the laboratory or pilot scale.

3、 Proficient in instrument operation: able to independently operate GC or GC-MS and possess solid spectral analysis skills. Familiarity with other analytical instruments is preferred.

4、 Data analysis skills: Possess good data sensitivity and analytical abilities, proficient in using data processing and plotting software such as Excel and Origin.

5、 Work meticulously and rigorously, with a strong sense of responsibility and teamwork spirit, possessing excellent communication skills. Strong safety awareness and strict adherence to laboratory operating procedures.

6、 Priority consideration criteria: those with specific project experience in evaluating Fischer Tropsch synthesis catalysts or analyzing products. Experience in testing or data analysis for conventional characterization of catalysts, such as BET, XRD, TPR/TPD. I have experience using Laboratory Information Management Systems (LIMS). Those with experience in building or modifying experimental equipment.

Salary and Benefits

Welfare: Five insurances and one fund, national and corporate annual leave, rental subsidies, vehicle subsidies, other benefits, etc.

1、 Implementation of process optimization algorithms: In response to the core requirements of high energy consumption, zero carbon green chemical industry to reduce energy consumption, improve yield, and enhance quality, an end-to-end algorithm solution is designed, including but not limited to: collection and organization of industrial data and establishment of feature engineering, process data correlation analysis, production constraint boundary analysis, yield and yield prediction, rolling process parameter optimization, closed-loop enhanced control, etc.

2、 Bridge between data and business: Deeply communicate and collaborate with process and production operation teams to transform business goals such as "reducing energy consumption by 5%" and "increasing yield by 2%" into algorithm implementable problems (such as feature definition and objective function design); At the same time, use language that other experts within the team can understand to interpret the algorithm results - for example, through SHAP value analysis, clarify that "temperature fluctuations" are the key factor affecting desorption rate, promote the optimization of equipment process parameters, and form a closed loop of "algorithm output → process improvement → value implementation".

3、 Algorithm iteration and engineering: tracking the performance of the model in actual field - if "model drift" is caused by changes in raw materials or deterioration of catalyst performance, continuously retrain and optimize with updated data; Improve the engineering performance of algorithms to meet the real-time adjustment needs of production lines.

4、 Cross team collaboration and internal value contribution: Externally, collaborate with equipment, materials, automation system vendors, and on-site teams to extend and integrate from the data-driven chain; Internally, integrating the algorithms and programs accumulated from practical experience into the company's internal platform system not only enables quick reuse of subsequent projects, but more importantly, forms the company's most core digital value asset.

1、 Basic conditions:

(1) Master's degree or above, major in computer science, automation, data science, applied mathematics or related fields, with at least 1 year of algorithm implementation experience (those with industrial scene experience are preferred, while those without experience but able to independently complete industrial data algorithm cases are also acceptable);

(2) Proficient in Python programming, proficient in time-series and relational databases, able to handle 100000 level time-series data;

(3) No adverse occupational record.

2、 Fundamentals of Algorithm Technology:

(1) Proficient in at least one mainstream machine learning/deep learning framework (TensorFlow/PyTorch), able to independently build time-series prediction (such as LSTM, Transformer, etc.), regression classification (such as XGBoost, LightGBM), and intelligent optimization (such as genetic algorithm, particle swarm optimization) models;

(2) Understanding the characteristics of industrial (especially chemical industry) data (multi noise, large time delay, multi variable coupling) can solve data quality problems through feature engineering (sliding window, time-domain/frequency-domain feature extraction, outlier processing), rather than just running models with "standard datasets".

3、 Industrial scene adaptation and on-site capabilities:

(1) To have a passion for industry, one must be able to go deep into the production line, understand the real production process, understand the systematic knowledge and fragmented experience descriptions of process and production experts, and transform them into data and algorithm models that can be characterized and analyzed;

(2) Accepting 30% of business trips, the company will establish testing and production lines in different regions in the future, which can adapt to the impact of factory site environment and surrounding environment.

4、 Practical thinking and communication skills:

(1) Not pursuing "complex algorithm skills", but focusing on "solving practical problems" - if the data volume of the pilot line is small, one can give up deep learning models that require a large amount of data and quickly implement them using traditional statistical learning+process rules methods, and first see the initial value;

(2) Being able to communicate with team members with non professional AI technology backgrounds in a value oriented manner, using the pragmatic statement 'this algorithm model can help reduce 2.3% of electrolysis efficiency fluctuations, equivalent to earning X million more' instead of the technical term 'we used Transformer self attention mechanism'.

5、 Internal Contribution Awareness:

Having the mindset of "sedimentation and reuse", after completing a specific project and business goal, being able to actively sort out reusable algorithm modules, tools, or experience documents, integrate them into the company's internal platform, save development time for the team's subsequent projects, and promote the large-scale output of the company's algorithm capabilities.

Salary and Benefits

Welfare: Five insurances and one fund, national and corporate annual leave, rental subsidies, vehicle subsidies, other benefits, etc.

1、 Auxiliary industrial data processing and feature engineering: Follow engineers to organize production data of green chemistry (such as temperature, pressure, energy consumption, yield data), assist in data cleaning (processing missing values, outliers), temporal data slicing (such as extracting sliding window features by production batch), assist in labeling "process anomaly data", and provide clean "input materials" for algorithm models.

2、 Writing and debugging of algorithm models: Under the guidance of engineers, train models - such as using XGBoost to assist in verifying the correlation between temperature fluctuations and desorption rates, or using LSTM to assist in testing the predictive performance of time-series data; Record the parameter changes and results during the debugging process, and help engineers organize the "Model Optimization Log".

3、 Cooperate with process communication and document accumulation: Follow engineers to participate in communication meetings with process and production teams, record "experience from process experts", and assist in transforming them into "feature descriptions understandable by algorithms"; At the same time, assist in organizing reusable tool documents (such as "Industrial Time Series Data Cleaning Steps" and "Basic Methods for Feature Filtering") to lay the foundation for the sedimentation of the company's internal platform.

4、 Participate in on-site research and data validation: Follow the team to the production line, observe the actual operational logic of the production line (such as the workflow of the electrolytic cell and the rhythm of raw material delivery), assist in recording the "differences between on-site data and system data", and avoid "disconnection between office data and actual production".

1、 Basic technical skills:

(1) Bachelor's degree or above (with priority given to first year graduate studies), major in computer science, automation, data science, applied mathematics, or chemical engineering (with a basic understanding of chemical engineering preferred);

(2) Proficient in programming with Python, familiar with basic data processing libraries (Pandas, NumPy), and knowledgeable in at least one machine learning framework (TensorFlow/PyTorch added points);

(3) Understand technical concepts such as "temporal data" and "feature engineering", and be able to understand "why industrial data needs to handle large time delay problems".

2、 Learning attitude and industrial sentiment:

(1) I am curious about "industrial production+AI" and willing to follow on-site (without excluding workshop environment and chemical production standards). I can patiently listen to process experts explain production logic;

(2) Not pursuing "showing off skills with complex algorithms", able to understand "solving small problems first (such as assisting in improving 0.1% yield), and then discussing big breakthroughs", willing to start with basic data analysis and document organization.

3、 Communication and collaboration skills:

(1) Be able to provide clear feedback to engineers on "problems encountered in data processing" (such as "how to handle a batch of data that is missing too much"), rather than blindly doing it;

(2) Can organize the 'model debugging results' in simple language (such as' after adjusting the window size, the prediction error decreased by 5%'), making it easier for the team to quickly understand.

4、 Time requirement:

Be able to work for 4 days or more per week and intern for at least 3 months (priority given to those who can intern for 6 months).

Internship salary: 220 yuan/day for undergraduate students, 260 yuan/day for master's students, and 300 yuan/day for doctoral students in first tier cities.

1、 Construction of Green Electricity Energy Storage Chemical Direct Connection Algorithm System:

(1) Design feasible AI solutions for the intermittency and volatility of photovoltaic/wind power, including short/medium/long-term prediction of green power (integrating meteorological data and power generation characteristics), and modeling of adjustable loads for chemical production lines (such as flexible load interval definition for electrolytic cells and various reactors);

(2) Develop source load collaborative optimization algorithms (multi-objective optimization, balancing green power consumption, production line yield, and production stability) and closed-loop control algorithms for direct connected systems (adjusting production line parameters in response to green power fluctuations in seconds).

2、 Cross system data integration and feature engineering:

(1) Lead the construction of data links between the green power side (inverter, SCADA system), energy storage side (BMS, PCS), and chemical side (DCS system), and process multi-source heterogeneous data (meteorological time-series data, real-time power generation data, energy storage status, production line process data);

(2) Design exclusive feature engineering for the time delay and coupling of "green power fluctuation production line response", such as green power fluctuation slope, production line load elasticity coefficient, voltage frequency deviation characteristics, etc.

3、 Stability and Engineering Implementation of Direct Connection Systems:

(1) Track the operational status of the green power chemical direct connection site and solve the problem of "model drift" (such as sudden changes in wind power, delayed response of energy storage PCS, catalyst activity affecting production line load, etc.);

(2) Optimize the real-time performance (ms) of the algorithm, adapt to the interface requirements of industrial control systems (such as DCS and PLC), and ensure the stable implementation of the solution from the test line to the production line.

4、 Cross team collaboration and technical accumulation:

(1) Deeply communicate with photovoltaic/wind power equipment manufacturers, energy storage system teams, and chemical process teams to transform business goals such as "improving green power consumption rate" and "controlling production line fluctuations" into algorithm implementable problems;

(2) Accumulate reusable algorithm modules (such as the General Model for Green Power Forecasting and the Flexible Load Control Toolkit), integrate them into the company's internal platform, and promote the large-scale replication of green power chemical direct connection technology.

1、 Basic conditions:

(1) Education: Master's degree or above, major in computer science, automation, data science, electrical engineering, energy and power, etc;

(2) Experience: More than 1 year of experience in implementing algorithms related to green power (photovoltaic/wind power) (such as power forecasting, load scheduling) or energy optimization in chemical production lines is preferred; Inexperienced individuals who can independently complete algorithm cases related to "green power load linkage" are also welcome;

(3) Tools: Proficient in Python programming, proficient in time-series databases and relational databases, able to handle millions of time-series data (green electricity+chemical dual end concurrent data);

(4) Other: No adverse occupational record.

2、 Core Technical Capability:

(1) Proficient in at least one mainstream machine learning/deep learning framework (TensorFlow/PyTorch), proficient in time-series prediction models (such as Transformer, LSTM+meteorological factor fusion model), multi-objective optimization algorithms (such as NSGA-III, improved particle swarm optimization), closed-loop control algorithms (such as model predictive control MPC), experience in green power prediction or industrial load regulation is preferred;

(2) Understand the characteristics of green electricity (output curves, fluctuation patterns, and influencing factors of photovoltaic/wind power), the control logic of large-scale energy storage PCS/SOC/BMS, and the load characteristics of chemical production lines (rigid/flexible load differentiation, key equipment start stop constraints, process parameter adjustment boundaries), and be able to handle the fusion problem of multi-source heterogeneous data;

(3) Familiar with at least one industrial communication protocol (such as OPC UA, Modbus TCP, IEC 61850), able to interface with SCADA systems on the green power side and DCS systems on the chemical side, with experience in building industrial data links or microgrid control, will earn extra points.

3、 On site and landing capabilities:

(1) Have a passion for industry, able to go deep into green power plants and chemical production lines, understand the operating logic of photovoltaic panels/fans, the response mechanism of energy storage PCS, and the load adjustment limits of chemical equipment, and avoid "brainstorming solutions in the office";

(2) Accepting 30% -40% business trips (for on-site debugging, data collection, and effect verification of green electricity chemical direct connection), able to adapt to the on-site environment of power plants and factories.

Communication and practical thinking:

(3) Not pursuing "complex algorithm skills", focusing on "solving practical problems" - for example, when green power data is scarce, it can combine mechanisms (such as photovoltaic irradiance power relationship) and a small amount of data to quickly build usable models; When there is severe fluctuation in green electricity, a multi-level scheme of "prediction+buffering+regulation" can be designed;

(4) Ability to communicate efficiently with teams with non AI backgrounds, using pragmatic language to replace purely technical jargon.

4、 Internal Contribution Awareness:

Having the mindset of "precipitation reuse", actively sorting out the parameter library of green power prediction models, production line load regulation rules, direct connection system interface specifications, etc. after the project is completed, saving development time for the team's subsequent projects.

Salary and Benefits

Welfare: Five insurances and one fund, national and corporate annual leave, rental subsidies, vehicle subsidies, other benefits, etc.

1、 Green electricity chemical dual end data processing and feature engineering:

(1) Follow engineers to organize photovoltaic/wind power data (irradiance, wind speed, power generation, voltage frequency) and chemical production line data (load, temperature, pressure, yield);

(2) Assist in completing data cleaning (processing outliers caused by green electricity fluctuations, timing alignment of dual end data), feature extraction (such as green electricity fluctuation amplitude, production line load elasticity, response delay features, etc.), and provide clean "input materials" for source load coordination algorithms.

2、 Assisted development and validation of direct connection related algorithms:

(1) Under the guidance of engineers, assist in building a short-term prediction model for green power and verifying the flexible range of production line loads (such as the relationship between reactor load regulation range and energy consumption);

(2) Record parameter adjustments and model performance (such as "reducing prediction error by X% after adding XX features"), and organize the "Model Optimization Log".

3、 Cross team communication and document accumulation:

(1) Follow engineers to attend communication meetings with green power equipment manufacturers and chemical process teams, record key information such as "green power generation constraints" and "production line load adjustment boundaries", and assist in transforming them into algorithmic understandable demand descriptions;

(2) Assist in organizing reusable tool documents (such as "Green Electricity Chemical Data Time Series Alignment Steps" and "Power Prediction Feature Selection Methods") to lay the foundation for the company's internal platform.

4、 On site research and data verification:

(1) Follow the team to the green power station and chemical production line, observe the operation status of green power equipment and the load adjustment logic of the chemical production line;

(2) Assist in recording the differences between green power side data and chemical side data, as well as the deviations between on-site fluctuations and system monitoring data, to avoid the disconnection between office data and actual linkage.

1、 Basic technical skills:

(1) Education: Bachelor's degree or above (with a preference for first year or above), major in computer science, automation, data science, applied mathematics, power engineering, electrical engineering, energy and power, chemical engineering or related fields (with a basic understanding of green electricity or power systems preferred);

(2) Programming: Proficient in Python programming, familiar with basic data processing libraries (Pandas, NumPy), and knowledgeable in at least one machine learning framework (TensorFlow/PyTorch is an added bonus);

(3) Cognition: Understand basic concepts such as "time series data", "green wave dynamics", and "production line load", and be able to understand "why green wave fluctuations affect the stability of chemical production lines".

2、 Learning attitude and cross-border sentiment:

(1) I am curious about "green power+chemical+AI" and willing to follow to green power plants and chemical workshops (not excluding industrial environments and complying with production standards);

(2) Can patiently listen to green power experts explain the logic of power generation and process experts explain the rules of energy consumption, not limited to "sitting in the office adjusting models";

(3) Not pursuing "complex algorithm skills", able to understand "solving small problems first (such as assisting in improving green electricity prediction accuracy), and then discussing big breakthroughs", willing to start from basic data analysis and document organization.

3、 Communication and collaboration skills:

(1) Can provide clear feedback to engineers on "problems encountered in data processing" (such as "how to align the timing of green power data and production line data"), instead of blindly doing it;

(2) Can organize the 'model debugging results' in simple language for the team to quickly understand.

4、 Time requirement:

Be able to work for 4 days or more per week and intern for at least 3 months (those who can intern for 6 months are preferred, and can deeply participate in on-site projects of green electricity chemical direct connection).

Internship salary: 220 yuan/day for undergraduate students, 260 yuan/day for master's students, and 300 yuan/day for doctoral students in first tier cities.

1、 Core technology development and mechanism research

Responsible for the development, optimization, and preparation process research of key materials such as high-performance and low-cost electrolytic water hydrogen production and CO ₂ electroreduction catalysts, electrodes, membrane electrodes, etc.

By utilizing electrochemical testing and in-situ characterization techniques, we aim to delve into the reaction and decay mechanisms, providing theoretical guidance for material and structural design.

2、 Electrolytic Cell Design and Engineering

Lead or participate in the structural design, simulation, and prototype manufacturing of electrolytic cells (single cells, stacks), with a focus on engineering issues such as mass transfer, conductivity, sealing, and durability.

Responsible for establishing a performance testing system for electrolytic cells, including evaluation and optimization of key indicators such as activity, efficiency, stability, and energy consumption.

3、 System integration and process amplification

Coordinate with system engineers to integrate and debug the electrolytic cell with subsystems such as gas supply, liquid management, and power control.

Explore and develop technical paths and process specifications from laboratory to pilot scale, and solve technical difficulties in the scaling process.

4、 Technical leadership and interdisciplinary collaboration

Lead or guide the R&D team to develop technical solutions and drive project milestones.

Work closely with material, chemical, mechanical, electrical, and automation teams to ensure consistency between research and development goals and product requirements.

Write high-quality technical reports, patents, and research papers to build the company's technological intellectual property barriers.

1、 Educational background:

Having a doctoral degree in electrochemistry, materials science and engineering, chemical engineering, applied chemistry, or a master's degree with at least 3 years of research and development experience in equivalent positions.

2、 Professional knowledge: Possess a strong foundation in electrochemistry and proficient in electrode process kinetics. Have solid experience in at least one of the fields:

(1) Hydrogen production direction: Deeply understand the key materials, components, and system knowledge in alkaline/PEM/high-temperature solid oxide electrolysis water technology.

(2) CO ₂ reduction direction: Familiar with CO generation pathways, proficient in catalyst design, membrane electrode construction, and product separation analysis techniques.

3、 Skills and experience:

(1) Proficient in the assembly, testing, and performance evaluation methods of electrolytic cells.

(2) Proficient in electrochemical workstations, impedance spectroscopy, and other testing techniques, and able to analyze products using gas/liquid chromatography.

(3) Having excellent data analysis and problem-solving skills, able to independently design experiments and analyze complex data.

(4) Having excellent reading and writing skills in both Chinese and English literature, as well as technical communication and expression abilities.

III. Priority consideration criteria

1、 Experience in R&D or industrialization projects related to electrolytic cells with pilot scale or above in well-known enterprises or laboratories.

2、 Having research and development experience in both hydrogen production and CO ₂ reduction fields, or having a deep understanding of one of them, and a strong desire to learn and apply in the other field.

3、 Publish high-level research papers in top academic journals, or hold relevant authorized invention patents as the main inventor.

4、 Having certain project management and team collaboration experience, able to lead small technical teams to overcome challenges.

Salary and Benefits

Welfare: Five insurances and one fund, national and corporate annual leave, rental subsidies, vehicle subsidies, other benefits, etc.