Computational

: Computational
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Title
Sector
Description
Title
Neural Networks
Sector
Computational
Description
Neural networks are computational models inspired by the brain, used in AI to process data, recognize patterns, and make predictions. They consist of interconnected layers of artificial neurons and are trained using large datasets. Modern neural networks power applications such as image recognition, language processing, and generative AI. Training them requires significant computing resources and careful data handling to avoid bias and ensure reliability.
Title
Numpy
Sector
Computational
Description
NumPy is a popular open-source Python library enabling efficient, large-scale numerical computing with multi-dimensional arrays and mathematical functions.
Title
Origin & Originpro
Sector
Computational
Description
Origin and OriginPro are the scientific graphing and data-analysis platforms — used by battery R&D teams to plot EIS data, cycle-life curves, DSC thermograms, and the dense multi-axis scientific figures that go into internal reports and publications. As a hard skill it requires fluency in non-linear curve fitting, custom function authoring (Origin C, LabTalk), template-based report generation, and the visualization vocabulary that battery chemistry papers expect (Bode plots, Nyquist plots, Ragone plots). R&D engineers apply this when characterizing a new cathode formulation's cycle life, when fitting EIS data to equivalent-circuit models, and when generating publication-quality figures for cell-development reports.
Title
Pandas
Sector
Computational
Description
Pandas is a Python library for data analysis and manipulation, offering flexible data structures like Series and DataFrames for tabular data.
Title
Power BI
Sector
Computational
Description
Microsoft Power BI is the business-intelligence platform of choice for battery operations that already live in the Microsoft 365 ecosystem — dashboards over MES data, supply chain analytics, quality KPIs, executive reporting. As a hard skill it requires fluency in DAX expressions, data modeling (star schemas, relationships), Power Query for ETL, and the publishing and permissioning model that gets dashboards into the right hands. Analysts apply this when building daily yield dashboards for plant managers, when joining MES data to ERP cost data for product-level margin analysis, and when surfacing field-warranty trends to engineering through filterable visuals.
Title
Programming, Coding & Development
Sector
Computational
Description
General software development practice — version control discipline, code review, testing, debugging, refactoring — underpins every codebase that touches battery work, from BMS firmware to MES code to data pipelines. As a hard skill it requires the habits of writing testable code, decomposing problems into small functions, reading other people's code without ego, and the judgment to know when "good enough" is right and when it isn't. Engineers across the battery org apply this daily — whether it's a controls engineer scripting a one-off data extract or a firmware lead architecting the BMS for the next pack platform.
Title
Prompt Engineering
Sector
Computational
Description
Prompt engineering is the practice of designing inputs to large-language models that reliably elicit accurate, well-formatted, and task-appropriate outputs, encompassing zero-shot, few-shot, chain-of-thought, and system-prompt strategies. As a hard skill it requires understanding how LLMs respond to instruction phrasing, role specification, output format constraints, and example demonstrations, as well as iteratively testing and refining prompts to reduce hallucination and improve consistency. Battery engineers, scientists, and analysts apply prompt engineering to get reliable results from AI assistants for code generation, literature synthesis, report drafting, data interpretation, and technical Q&A within automated pipelines.
Title
Pytorch
Sector
Computational
Description
PyTorch is an open-source deep learning library by Meta, known for its easy-to-use API, GPU acceleration, and popularity in AI research.
Title
Python
Sector
Computational
Description
Python is the de facto language for battery data work — analytics on formation data, ML model training, factory-floor scripting, test automation, and the glue code that ties MES to ERP to the data lake. As a hard skill it requires fluency in the scientific stack (numpy, pandas, scipy, scikit-learn), the visualization libraries (matplotlib, seaborn, plotly), packaging and venv discipline, and the asyncio-vs-threading-vs-multiprocessing tradeoffs for production work. Engineers apply this when cleaning a million-row cell-formation CSV in pandas, when training an ML model to flag electrode-coating defects, and when writing the integration script that pulls daily MES exports into the analytics warehouse.
Title
R
Sector
Computational
Description
R is the statistical-computing language favored by quality engineers and academic-leaning battery research teams — used for advanced statistical modeling, mixed-effects models on cell-aging data, and the visualization work where ggplot has no real peer. As a hard skill it requires fluency in tidyverse idioms (dplyr, tidyr, ggplot2), statistical model interpretation (lm, glm, mixed models), R Markdown for reproducible reports, and the package ecosystem that surrounds it (CRAN, Bioconductor). Statisticians and quality engineers apply this when running a designed experiment on cell-coating parameters, when modeling battery capacity fade with random effects across cell lots, and when authoring reproducible analysis reports that ship to regulators.
Title
Raspberry Pi
Sector
Computational
Description
Raspberry Pi is the small-form-factor Linux computer that battery teams reach for when they need an edge node — datalogging on a benchtop cycler, an MQTT gateway between an old PLC and a modern data lake, an in-fixture vision system for cell-tab inspection. As a hard skill it requires fluency in Raspberry Pi OS, GPIO control via Python or C, MQTT and OPC-UA client libraries, and the disciplines around running a non-ruggedized board in an industrial environment (power conditioning, watchdog timers, remote-reset hardware). Engineers apply this when bridging a legacy benchtop instrument to a modern data pipeline, when prototyping a vision-based defect detector, or when adding a cheap data-collection node next to a piece of equipment whose vendor SDK is too expensive to license.
Title
Retrieval-augmented Generation (RAG)
Sector
Computational
Description
Retrieval-augmented generation (RAG) lets AI models pull in external data to improve accuracy, reduce hallucinations, and cite sources.
Title
SOLIDWORKS
Sector
Computational
Description
SolidWorks is the Dassault Systèmes 3D mechanical CAD platform dominant in US battery manufacturing — used for pack enclosure design, fixture design for cell-tab welding, BMS PCB carriers, and the broad mechanical work of factory tooling. As a hard skill it requires fluency in parametric part and assembly modeling, drawing generation to ASME Y14.5, sheet-metal and weldment environments, and the PDM disciplines (PDM Standard or Professional) that keep revisions clean across engineering teams. Mechanical designers apply this when iterating a battery pack enclosure for a new vehicle program, when generating shop drawings for a stamped current collector, and when running interference and tolerance studies on a multi-cell module assembly.
Title
Scala
Sector
Computational
Description
Scala is the JVM-hosted functional/object-oriented language that powers Spark — the dominant distributed compute framework used by battery teams running large-scale analytics on cell-formation, end-of-line test, and fleet telemetry data. As a hard skill it requires fluency in Scala's type system (case classes, traits, implicits), the functional idioms (immutable data, higher-order functions), Spark's DataFrame and Dataset APIs, and the build and dependency tooling (sbt, Maven). Data engineers apply this when writing Spark jobs that join millions of cell-formation records with corresponding pack tests, when building streaming ingestion from Kafka into the data lake, and when refactoring Python prototypes into production-grade Spark for plant-wide deployment.
Title
Sigmaplot
Sector
Computational
Description
SigmaPlot is the scientific-graphing package historically favored in life sciences and academic battery research — used to produce publication-grade figures of cycle life, Coulombic efficiency, and cell-aging curves. As a hard skill it requires fluency in non-linear regression model fitting, transform-based axis configuration (log-log Ragone plots, Arrhenius plots), template authoring, and the export discipline (vector formats, font embedding) that journals require. Research scientists apply it when characterizing a new electrolyte formulation's cycle stability, when fitting capacity-fade data to power-law or exponential decay models, and when generating figures for cell-development internal reports.
Title
Stata
Sector
Computational
Description
Stata is a statistical software package for data analysis, widely used in research, known for extensibility, user community, and frequent updates.
Title
Structured Query Language (SQL)
Sector
Computational
Description
SQL is the universal language for relational data — battery production teams use it to query MES databases, pull cell-formation history, audit pack genealogy, and feed analytics platforms with structured data. As a hard skill it requires fluency in joins (inner, outer, anti), window functions, aggregation patterns, query plan reading, and the index-design discipline that determines whether a yield query returns in 200ms or 20 minutes. Engineers and analysts apply this when reconstructing a cell's life from manufacture to field failure, when pulling a daily yield extract for the morning standup, and when designing the schema that ties pack IDs to cell IDs to test records.
Title
Sympy
Sector
Computational
Description
SymPy is a free, open-source Python library for symbolic mathematics, offering computer algebra capabilities and a wide range of math tools.
Title
Tableau
Sector
Computational
Description
Tableau is the visualization platform favored by analytics teams that want polished, interactive dashboards without writing front-end code — used for battery yield dashboards, quality KPIs, supply-chain analytics, and executive reporting. As a hard skill it requires fluency in data prep (Tableau Prep), calculated-field authoring (LOD expressions, table calcs), dashboard design for non-technical audiences, and the publishing model (Tableau Server/Cloud) that gets dashboards into stakeholders' hands. Analysts apply this when building a yield-trend dashboard for plant managers, when creating an interactive defect-Pareto for quality reviews, and when surfacing field-warranty data to product engineering through filterable visuals.
Title
Tensorflow
Sector
Computational
Description
TensorFlow is a popular, open-source machine learning library developed by Google. It supports multiple languages and platforms.
Title
VBA
Sector
Computational
Description
Visual Basic for Applications (VBA) is the macro language that runs inside Microsoft Office — battery quality teams still use it heavily for Excel-based test reports, automated PPAP documentation, and the one-off automations that bridge between an analyst's workbook and a vendor's data extract. As a hard skill it requires fluency in the Excel object model (Workbooks, Worksheets, Ranges, charts), the editor's debugging tools, and the discipline of writing macros that won't break when the source data shape shifts. Analysts apply this when automating the daily-yield rollup in Excel, when generating PPAP test report templates from raw cell-formation data, and when stitching Excel reports together for engineering reviews where Tableau and Power BI aren't available.
Title
Verilog & VHDL
Sector
Computational
Description
Verilog and VHDL are the hardware description languages used to design the FPGAs and ASICs that increasingly appear inside battery management systems — for high-speed cell-balancing, parallel cell-voltage sampling, and the real-time fault-detection logic that microcontrollers can't service fast enough. As a hard skill it requires fluency in synthesizable HDL subsets, testbench authoring, simulator workflows (ModelSim, Verilator, Vivado), and the timing-closure discipline that distinguishes hobby HDL from production silicon. Hardware engineers apply this when designing the FPGA front-end that samples 96 cells simultaneously on a pack controller, when prototyping an ASIC for next-generation BMS, and when implementing the safety-monitor logic that watchdogs the BMS microcontroller.
Title
Vibe Coding
Sector
Computational
Description
Vibe coding is AI-driven programming where prompts to language models produce code, prioritizing results over review and deep code understanding.
Title
Vienna Ab Initio Simulation Package (VASP)
Sector
Computational
Description
VASP is a Fortran-based package for ab initio quantum mechanical calculations, using DFT and advanced methods, widely used in materials science.
Title
Scikit-learn
Sector
Computational
Description
Scikit-learn is a leading open-source Python library for machine learning, widely used in industry and academia for diverse data analysis tasks.
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