Applied Machine Learning & Process Modeling Engineer (Water Treatment)
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Applied Machine Learning & Process Modeling Engineer (Water Treatment)
Location
CHE - Solothurn - Witterswil
Experience
Mid
Posted
Jul 7, 2026
Apply by
August 6, 2026
Applicants
0
Early applicantEasy applyFull-timeWork from Office
Job Description
Job Description
This role sits at the intersection of **process engineering, applied mathematics, and machine learning**, with a strong focus on **real-world industrial impact**.
You will work closely with engineers and plant operators to:
- Improve operational efficiency
- Reduce cost and environmental footprint
- Increase understanding and robustness of our systems
**Responsibilities**
- Develop **predictive models** for water treatment processes (e.g., ACF, IEX, RO)
- Data-driven models (regression, tree-based methods, etc.)
- Physics-informed or hybrid models
- Translate **operational data into actionable insights**
- Build simple, robust models that can be used by **engineering and operations teams**
- Analyze plant performance to identify **optimization opportunities**
- Work with multidisciplinary teams (process, automation, software)
- Support deployment of models into engineering tools or operational workflows
- Participate in **on-site visits internationally** to understand systems and validate models
**Qualifications**
We are looking for a **hands-on problem solver** who can bridge theory and real-world systems.
**Must-have**
- MSc or PhD in:
- Applied Mathematics, Data Science, Chemical Engineering, Environmental Engineering, or similar
- 2+ years of experience
- Strong understanding of **industrial water processes and field applications**
- Good programming skills (Python preferred: pandas, NumPy, scikit-learn, etc.)
- Solid understanding of:
- Modelling / simulation (deterministic or statistical)
- Data analysis and signal interpretation
- Ability to work with **noisy real-world data** (not clean academic datasets)
- Fluent in English
**Strong plus**
- Experience with:
- Water treatment or process industries
- Time-series analysis, control systems, or optimization
- First-principles modeling (mass balance, transport phenomena)
- Familiarity with:
- Hybrid modeling (physics + ML)
- Deployment of models into production tools
- Ability to communicate effectively with **non-data specialists (operators, engineers)**
**What makes this role unique**
- Work on **real industrial systems**, not abstract datasets
- Direct impact on **sustainability and water circularity**
- Opportunity to shape the **digital transformation of water treatment plants**
- Combination of:
- Field understanding
- Engineering
- Data science
Key Responsibilities
- Develop predictive models for water treatment processes such as ACF, IEX, and RO
- Translate operational data into actionable insights for engineering and operations teams
- Analyze plant performance to identify optimization opportunities
- Support the deployment of models into engineering tools or operational workflows
- Participate in international on-site visits to validate models and understand systems
Requirements
- MSc or PhD in Applied Mathematics
- Data Science
- Chemical Engineering
- Environmental Engineering
- or similar
Skills Required
PythonpandasNumPyscikit-learnModelingSimulationData analysisSignal interpretationProblem solvingCommunicationCollaborationTime-series analysisControl systemsOptimizationFirst-principles modelingMass balanceTransport phenomenaHybrid modelingProduction tool deploymentCommunication with non-data specialists
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