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Public Health ) at Tampere University. Previous experience in statistical programming, particularly R, and working with large-scale registry or longitudinal data will be considered an advantage
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(but not required): experience with surface‑sensitive techniques, synthetic and preparative organic chemistry, lignin analytics, colloids, polymer processing, programming (e.g., Python/R/Matlab
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codes (especially Molecular Dynamics with codes such as GROMACS and LAMMPS), as well as analysis and visualization. Experience in scientific programming and numerical simulations (e.g. Python
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required): experience with surface‑sensitive techniques, synthetic and preparative organic chemistry,lignin analytics, colloids, polymer processing, programming (e.g., Python/R/Matlab), molecular
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, Python or similar programming languages. Previous research experience with toolkits for quantum computing such as Qiskit, QuTiP, PennyLane, Quil, etc. Familiarity with the firmware of various control
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numerical skills especially in Matlab, Mathematica, Python or similar programming languages. Previous research experience with toolkits for quantum computing such as Qiskit, QuTiP, PennyLane, Quil, etc
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learning, particularly physics-informed or physics-constrained learning Programming and modelling skills (Matlab/Simulink, Python, C or other embedded-systems languages) Interest in embedded systems and
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data (an interest or aptitude here is enough; we don’t expect prior demonstrated experience) Good programming skills – you are required to provide a sample of your code during the recruitment process
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measured data (an interest or aptitude here is enough; we don’t expect prior demonstrated experience) Good programming skills – you are required to provide a sample of your code during the
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estimation or optimization methods (e.g. Kalman filtering, convex optimization) Interest or experience in machine learning, particularly physics-informed or physics-constrained learning Programming