Founding Hands-on Scientist - Physics, Geometry, Ontology & AI
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This is not a conventional salaried research position.
It is an equity-first founding opportunity for an exceptional scientist with hands-on industrial experience who wants to help build a deep-tech company from the ground up.
Neodustria is a Swiss deep-tech venture headquartered at the Switzerland Innovation Park in the Canton of Jura, building a new generation of Industrial Intelligence at the intersection of:
Physics × Engineering × Geometry × Artificial Intelligence × Industrial Data
We are looking for an exceptional Founding Scientist to help define the scientific foundations of the platform.
You may bring exceptional depth in one core domain and complementary strength in the others.
We are not looking for someone who simply studies the problem. We are looking for someone who wants to build the solution.
The Founder Track 🚀
This opportunity is part of Neodustria’s Founder Track. The initial phase is equity-first.
You contribute through a structured collaboration and sweat-equity arrangement while we evaluate:
- Scientific excellence
- Technical execution
- Ownership
- Entrepreneurial mindset
- Long-term alignment
- Ability to transform research into industrial technology
Subject to milestones, financing, contribution and mutual agreement, the role will later transition into a full-time scientific leadership position combining salary + long-term equity.
Leadership is earned through execution, not promised in advance.
Physics
We are interested in scientists working across areas such as:
- Computational physics
- CFD
- FEA
- Thermal modelling
- Multiphysics
- Numerical PDEs
- Reduced-order modelling
- Scientific computing
- Solver development and orchestration
- Verification, validation and uncertainty quantification
Experience with OpenFOAM, CalculiX, SU2, FEniCS, PETSc, deal.II or equivalent environments is valuable. But we care more about whether you understand the mathematics, numerical assumptions and physical validity behind the tools.
3D Geometry
Industrial intelligence cannot reason correctly about an engineering object without understanding its geometry.
Relevant areas include:
- Computational geometry
- CAD kernels
- B-Rep topology
- Parametric geometry
- Surface and solid modelling
- Feature recognition
- Geometry repair and defeaturing
- Meshing
- CAD-to-simulation workflows
- Geometry comparison
- Spatial algorithms
Experience with OpenCascade / OCCT, CGAL, Gmsh, STEP, IGES, STL, C++, Python or WebAssembly is particularly relevant.
We value scientists who understand the difference between CAD geometry, mathematical surfaces, discrete meshes and simulation-ready representations.
Ontology & Knowledge Representation
We are also interested in scientists with experience in industrial ontologies, semantic knowledge representation and knowledge graphs, including RDF, RDFS, OWL, SPARQL, SHACL and graph-based reasoning.
The challenge is to represent engineering entities, geometry, materials, physics, requirements, evidence and their relationships in a form that is machine-readable, explainable and causally useful, allowing Neodustria to connect fragmented industrial knowledge into a coherent reasoning layer.
AI
We are not interested in placing a generic chatbot on top of engineering software.We are developing systems that can reason across:
Geometry → Materials → Physics → Simulation → Evidence → Decision
Relevant areas include:
- Physics-informed machine learning
- Physics-constrained learning
- Scientific ML
- Neural operators
- Graph neural networks
- Geometric deep learning
- Mesh and point-cloud learning
- Surrogate modelling
- Differentiable simulation
- Reduced-order learning
- Scientific foundation models
- Hybrid physics-ML systems
- Uncertainty-aware AI
One of our central questions is:
How can machine intelligence learn from physics without replacing physical truth?
We Need Scientists Who Build
This is not a research-only role. You should be comfortable:
- Formulating scientific problems
- Developing mathematical approaches
- Writing code
- Implementing numerical methods
- Building experiments
- Integrating solvers
- Processing CAD and meshes
- Training scientific ML models
- Creating benchmarks
- Testing physical validity
- Analysing failures
- Turning research into reusable technology
At Neodustria, the boundary between scientist and engineer is intentionally thin.
Who We Are Looking For
We care far more about execution than titles. We want people who:
- Think from first principles
- Remain deeply hands-on
- Challenge assumptions
- Thrive in ambiguity
- Take ownership
- Build rather than supervise
- Think entrepreneurially
- Want to create technology from zero
- Are comfortable taking genuine founder-level risk
If your preferred contribution is only advisory, managerial or academic, this is probably not the right opportunity.
A PhD or equivalent research experience in applied mathematics, computational physics, mechanics, engineering, computational geometry, scientific computing or AI is strongly valued.
But exceptional engineering achievements, open-source work, scientific software, industrial deployments and demonstrated execution matter just as much.
Industrial Experience
Experience in engineering-intensive sectors is particularly valuable:
Automotive | Aerospace | Aviation | Railway | Naval | Construction | Energy | Robotics | Industrial Equipment | Micro-Precision | Advanced Manufacturing
Understanding how engineers actually work matters as much as understanding the science.
Location
📍 Headquarters: Switzerland Innovation Park, Canton of Jura, Switzerland
🌍 Work model: Remote or hybrid during the founding phase, depending on the collaboration.
English is expected for scientific and international work.
Why Neodustria?
Industrial engineering remains fragmented.
CAD understands geometry.
Simulation understands physics.
PLM understands engineering data.
AI understands patterns.
Very few systems understand how all of these domains influence each other. Neodustria is building that missing layer.
Our platform connects:
3D Geometry Intelligence
Physics & Multiphysics Simulation
Materials Intelligence
Physics-Aware AI
Engineering Knowledge
Quality Intelligence
Industrial Economics
into a unified Industrial Intelligence environment.
Exceptional Hands-on Scientists with entrepreneur mindset
You should not apply if your priority is immediate salary security.
You should not apply if you want only a predefined research agenda.
You should not apply if you want to supervise rather than build.
This is for people willing to take founder-level technical and financial risk in exchange for founder-level ownership potential.