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Dr. Alexander Henkes AI Development & Solutions Zürich
Data Scientist
June 29, 2026 – Present
planetarium
November 30, 2025 – November 30, 2025
A real-time, terminal-based solar system simulator written in Rust.
View ProjectSemester-Project
September 17, 2024 – December 14, 2024
Semester project in the Computational Mechanics Group (Prof. Dr. Laura De Lorenzis, ETH Zurich). The project aims to efficiently implement Kolmogorov-Arnold Neural Networks (KANNs) using the Torch C++ backend
View ProjectOptimalControlPDEAutoDiff
August 19, 2024 – August 21, 2024
Optimal Control with PDEs solved by a Differentiable Solver
View ProjectHENKES_SNN
August 20, 2022 – April 9, 2024
Code of the publication "Spiking neural networks for nonlinear regression" published in https://doi.org/10.1098/rsos.231606 by Alexander Henkes from ETH Zürich, Jason K. Eshraghian from University of California, Santa Cruz and Henning Wessels from TU Braunschweig.
View ProjectHENKES_GAN
July 27, 2022 – August 13, 2022
Code of the publication "Three-dimensional microstructure generation using generative adversarial neural networks in the context of continuum micromechanics" published in https://doi.org/10.1016/j.cma.2022.115497 by Alexander Henkes and Henning Wessels from TU Braunschweig.
View ProjectHENKES_PINN
July 14, 2022 – July 28, 2022
Code of the publication "Physics informed neural networks for continuum micromechanics" published in https://doi.org/10.1016/j.cma.2022.114790 by Alexander Henkes and Henning Wessels from TU Braunschweig and Rolf Mahnken from University of Paderborn.
View Projectsnntorch
September 28, 2020 – Present
Deep and online learning with spiking neural networks in Python
View ProjectCultural Fit Analysis
The candidate's projects are heavily research-oriented and academic, focusing on advanced neural network architectures and scientific computing. While this demonstrates deep technical expertise, the breadth of application areas outside of academic research is limited. The target role is 'Data Scientist', and while the candidate possesses strong ML/AI skills, the resume does not clearly show experience in typical data science workflows such as data cleaning, feature engineering, model deployment in production, or business-oriented problem-solving. The single listed professional experience is self-employment, which provides limited insight into corporate cultural fit or team dynamics.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a strong drive for research and publication, suggesting self-motivation and a focus on deep technical problems. The nature of the projects implies strong problem-solving skills and an ability to work independently on complex challenges. However, there is no direct data to assess collaboration, communication, or stress handling in a team environment.