Multidisciplinarity is important in R&D and a core feature of my education.
My skills are sorted by my competence in them. This is of course subjective and must be interpreted relatively rather than absolutely.
Excellent
Core concentration with many in-depth courses for each area
- Simulation & modelling
- Semiconductor & solid-state physics
- Electromagnetism
- Quantum mechanics
- Mathematics
- Classical mechanics (not mechanical engineering)
- Python & MATLAB
- Maple
- LaTeX
Good
Solid understanding of a subject
- Machine learning & statistics
- Photonics
- Quantum optics
- EM and US waves in living matter
- Exposure, imaging, therapy & neural interaction
- Object-oriented programming
- C++
- Signal processing
- Control theory
- Transport phenomena
- Fluid, heat and mass flow
- Thermodynamics
- Physical chemistry & kinetics
- Molecular modelling
- Ab initio QM to macroscopic properties on a HPC
- Statistical mechanics
Average
More than introductory, reasonable grasp of an area
- Discrete mathematics
- Antennas and antenna arrays
- Includes NEC: antenna modelling & optimising software
- Github
- Nuclear physics
- Plasma physics
- High and low temperature
- Acoustics
- Optimisation
- Inorganic chemistry reactions
- Analog electronics
- Industrial material science
- Ceramics, polymers, metals, ...
- Mechanical properties/fabrication of materials
Basic
Seen as (small) part of one course, familiar with terminology and broad lines of the area
- Digital electronics
- Organic chemistry
- Chip-design
- Arduino
- Lua
- CFD
- Economy and business administration
- Mechanical engineering
- Data science