Prof. Dr. Dirk Wilhelm

Arbeit an der ZHAW

Tätigkeit

• Unterricht im BSc: Numerik für Maschinentechnik und Systemtechnik, Medizintechnik
• Unterricht im MSc: OpenFoam EVA
• F&E: Nuclear Magnetic Resonance (NMR), Computational Fluid Dynamics (CFD), Numerik
• Direktor School of Engineering

Berufserfahrung

• Externer Dozent
  Universität Zürich
  2020 - heute
• Direktor ZHAW School of Engineering
  ZHAW
  2019 - heute
• Professor Medizinphysik
  ZHAW
  2016 - heute
• Leiter Abteilung für Angewandte Mathematik, Physik, Systems and Operations
  ZHAW
  2013 - 2018
• Abteilungsleiter F&E
  Bruker BioSpin
  2003 - 2013
• Entwicklungsingenieur
  Alstom
  2001 - 2003
• Postdoc
  UC Santa Barbara
  2000 - 2001

Netzwerk

Mitglied in Netzwerken

• COST Mathematics for Industry Network
• Innovationspanel der DIZH (Digitalisierungsinitiative des Kanton Zürich)
• Winlink (Präsident)
• Swiss Engineering STV
• VDI
• FTAL (Präsident)
• DPG
• ASME

ORCID digital identifier

ORCID ID: 0000-0001-5109-9803

Projekte

• Smart Acquisition for Ultra-High field NMR Spectroscopy / Teammitglied / laufend
• NMR-based drug discovery / Teammitglied / laufend
• PhD Program in Data Science / Co-Projektleiter:in / abgeschlossen
• Europäische Konferenzserie zu Künstlicher Intelligenz (KI) in Industrie und Finanzwesen / Teammitglied / abgeschlossen
• Maschinelles Lernen für NMR-Spektroskopie / Stellv. Projektleiter:in / abgeschlossen
• SCIS Simulation Based Calibration of Infusion Systems / Teammitglied / abgeschlossen
• 3rd European COST Conference on Mathematics for Industry in Switzerland / Teammitglied / abgeschlossen
• PhD Network in Data Science / Projektleiter:in / abgeschlossen
• 2nd European COST Conference on Mathematics for Industry in Switzerland / Teammitglied / abgeschlossen
• 1st European COST Conference on Mathematics for Industry in Switzerland / Teammitglied / abgeschlossen
• Entwicklung eines Ultra-Low-Temperature NMR Probenkopfes für hochgeschwindigkeits Magic-Angle-Spinning Anwendungen mit Hilfe von Computational Fluid Dynamics (CFD) Simulationen / Projektleiter:in / abgeschlossen

Publikationen

Beiträge in wissenschaftlicher Zeitschrift, peer-reviewed

• Fischetti, G., Schmid, N., Bruderer, S., Heitmann, B., Henrici, A., Scarso, A., Caldarelli, G., & Wilhelm, D. (2025). A deep learning framework for multiplet splitting classification in 1HNMR. Journal of Magnetic Resonance, 373(107851). https://doi.org/10.1016/j.jmr.2025.107851
• Hostettler, M., Grüter, R., Stingelin, S. I., De Lorenzi, F., Füchslin, R. M., Jacomet, C., Koll, S., Wilhelm, D., & Boiger, G. K. (2023). Modelling of peristaltic pumps with respect to viscoelastic tube material properties and fatigue effects. Fluids, 8(9), 254–269. https://doi.org/10.3390/fluids8090254
• Schmid, N., Bruderer, S., Paruzzo, F., Fischetti, G., Toscano, G., Graf, D., Fey, M., Henrici, A., Ziebart, V., Heitmann, B., Grabner, H., Wegner, J. D., Sigel, R. K. O., & Wilhelm, D. (2023). Deconvolution of 1D NMR spectra : a deep learning-based approach. Journal of Magnetic Resonance, 347(107357). https://doi.org/10.1016/j.jmr.2022.107357
• Fischetti, G., Schmid, N., Bruderer, S., Caldarelli, G., Scarso, A., Henrici, A., & Wilhelm, D. (2023). Automatic classification of signal regions in 1H nuclear magnetic resonance spectra. Frontiers in Artificial Intelligence, 5(1116416). https://doi.org/10.3389/frai.2022.1116416
• Herzog, N., Weber, A. S., Purea, A., Osen, D., Knott, B., Engelke, F., & Wilhelm, D. (2022). Ultra low temperature microturbine for magic angle spinning system. Journal of Fluids Engineering, 144(8), 081205–081201. https://doi.org/10.1115/1.4053746
• Cousin, S., Charlier, C., Kadeřávek, P., Marquardsen, T., Tyburn, J.-M., Bovier, P.-A., Ulzega, S., Speck, T., Wilhelm, D., Engelke, F., Maas, W., Sakellariou, D., Bodenhausen, G., Pelupessy, P., & Ferrage, F. (2016). High-resolution two-field nuclear magnetic resonance spectroscopy. Physical Chemistry Chemical Physics, 18(48), 33187–33194. https://doi.org/10.1039/C6CP05422F
• Herzog, N., Wilhelm, D., Koch, S., Purea, A., Osen, D., Knott, B., & Engelke, F. (2016). Aerodynamic optimization of a microturbine inserted in a magic-angle spinning system. Journal of Fluids Engineering, 138(12), 121106.
• Wilhelm, D., Purea, A., & Engelke, F. (2015). Fluid flow dynamics in MAS systems. Journal of Magnetic Resonance, 257, 51–63. https://doi.org/10.1016/j.jmr.2015.05.006
• Meiburg, E., Vanaparthy, S. H., Payr, M. D., & Wilhelm, D. (2004). Density-driven instabilities of variable-viscosity miscible fluids in a capillary tube. Annals of the New York Academy of Sciences, 383–402. https://doi.org/10.1196/annals.1324.032
• Wilhelm, D., & Meiburg, E. (2004). Three-dimensional spectral element simulations of variable density and viscosity, miscible displacements in a capillary tube. Computers & Fluids, 33(3), 485–508. https://doi.org/10.1016/S0045-7930(03)00059-8
• Vanaparthy, S. H., Meiburg, E., & Wilhelm, D. (2003). Density-driven instabilities of miscible fluids in a capillary tube : linear stability analysis. Journal of Fluid Mechanics, 497, 99–121. https://doi.org/10.1017/S0022112003006499
• Wilhelm, D., Härtel, C., & Kleiser, L. (2003). Computational analysis of the two-dimensional–three-dimensional transition in forward-facing step flow. Journal of Fluid Mechanics, 489, 1–27. https://doi.org/10.1017/S0022112003004440
• Jenny, B., Wilhelm, D., & Valero-Garcés, B. L. (2003). The southern westerlies in central chile : holocene precipitation estimates based on a water balance model for Laguna Aculeo (33°50′S). Climate Dynamics, 20(2-3), 269–280. https://doi.org/10.1007/s00382-002-0267-3
• Wilhelm, D., & Kleiser, L. (2002). Application of a spectral element method to two-dimensional forward-facing step flow. Journal of Scientific Computing, 17(1-4), 619–627. https://doi.org/10.1023/A:1015178831786
• Kleiser, L., & Wilhelm, D. (2001). Stability analysis for different formulations of the nonlinear term in PN−PN−2 spectral element discretizations of the navier–stokes equations. Journal of Computational Physics, 174(1), 306–326. https://doi.org/10.1006/jcph.2001.6912
• Wilhelm, D., & Kleiser, L. (2000). Stable and unstable formulations of the convection operator in spectral element simulations. Applied Numerical Mathematics, 33(1-4), 275–280. https://doi.org/10.1016/S0168-9274(99)00093-8
• Wilhelm, D., Härtel, C., & Eckelmann, H. (1998). On the relation between fronts and high-shear layers in wall turbulence. Flow, Turbulence and Combustion, 60(1), 87–103. https://doi.org/10.1023/A:1009952224076

Bücher, peer-reviewed

Wilhelm, D. (2000). Numerical investigation of three-dimensional separation in a forward-facing step flow using a spectral element method [Doctoral dissertation, ETH Zürich]. https://doi.org/10.3929/ethz-a-004037003

Schriftliche Konferenzbeiträge, peer-reviewed

• Fischetti, G., Schmid, N., Bütikofer, M., Torres, F., Henrici, A., & Wilhelm, D. (2025, July 6). Fully automated analysis of photo-CIDNP NMR spectra for fast fragment screening. 21st European Magnetic Resonance Congress (EUROMAR), Oulu, Finnland, 6-10 July 2025. https://doi.org/10.21256/zhaw-33657
• Schmid, N., Wanner, M., Fischetti, G., Meshkian, M., Bruderer, S., Henrici, A., Füchslin, R. M., Wegner, J. D., Sigel, R. K. O., Heitmann, B., & Wilhelm, D. (2025, July 6). MolDETR : next-generation analysis of molecular spectra with deep learning. 21st European Magnetic Resonance Congress (EUROMAR), Oulu, Finnland, 6-10 July 2025. https://doi.org/10.21256/zhaw-33659
• Schmid, N., Wanner, M., Fischetti, G., Meshkian, M., Bruderer, S., Henrici, A., Wegner, J. D., Sigel, R. K. O., Heitmann, B., & Wilhelm, D. (2024, September 12). Automated spin system analysis in NMR spectroscopy with SpinDETR : a deep learning approach. Datalab-Symposium, Winterthur, Schweiz, 12. September 2024. https://doi.org/10.21256/zhaw-31443
• Fischetti, G., Schmid, N., Bruderer, S., Henrici, A., Heitmann, B., Scarso, A., Caldarelli, G., & Wilhelm, D. (2024, September 12). MuSe Net : a deep learning framework for trustworthy multiplet segmentation in 1D 1H NMR spectra. Datalab-Symposium, Winterthur, Schweiz, 12. September 2024. https://doi.org/10.21256/zhaw-31444
• Fischetti, G., Schmid, N., Bruderer, S., Henrici, A., Heitmann, B., Scarso, A., Caldarelli, G., & Wilhelm, D. (2024, July 2). MuSe Net : a deep learning framework for trustworthy multiplet segmentation in 1D 1H NMR spectra. 20th European Magnetic Resonance Congress (EUROMAR), Bilbao, Spain, 30 June - 4 July 2024. https://doi.org/10.21256/zhaw-31102
• Schmid, N., Wanner, M., Fischetti, G., Meshkian, M., Bruderer, S., Henrici, A., Wegner, J. D., Sigel, R. K. O., Heitmann, B., & Wilhelm, D. (2024, July 2). Automated spin system analysis in NMR spectroscopy with SpinDETR : a deep learning approach. 20th European Magnetic Resonance Congress (EUROMAR), Bilbao, Spain, 30 June - 4 July 2024. https://doi.org/10.21256/zhaw-31101
• Schmid, N., Fischetti, G., Henrici, A., Wilhelm, D., Wanner, M., Meshkian, M., Bruderer, S., Wegner, J.-D., Sigel, R. K. O., Heitmann, B., & Konukoglu, E. (2023). Transforming NMR spectroscopy : extraction of multiplet parameters with deep learning [Conference poster]. In T. Prisner (Ed.), Euromar 2022 Abstractbook (p. 291). ZHAW Zürcher Hochschule für Angewandte Wissenschaften. https://doi.org/10.21256/zhaw-29510
• Fischetti, G., Schmid, N., Henrici, A., Wilhelm, D., Bruderer, S., Heitmann, B., Scarso, A., & Caldarelli, G. (2023). Uncertainty quantification for reliable automatic multiplet classification in 1H NMR spectra [Conference poster]. In T. Prisner (Ed.), Euromar 2023 Programme & Abstract Book (p. 350). ZHAW Zürcher Hochschule für Angewandte Wissenschaften. https://doi.org/10.21256/zhaw-29538
• Schmid, N., Bruderer, S., Fischetti, G., Paruzzo, F., Toscano, G., Graf, D., Fey, M., Ziebart, V., Henrici, A., Grabner, H., Wegner, J. D., Sigel, R. K. O., Heitmann, B., & Wilhelm, D. (2023, January 11). Deconvolution of NMR spectra : a deep learning-based approach. Datalab Symposium, Winterthur, Schweiz, 11. Januar 2023. https://doi.org/10.21256/zhaw-27429
• Fischetti, G., Schmid, N., Bruderer, S., Paruzzo, F., Toscano, G., Graf, D., Fey, M., Henrici, A., Scarso, A., Caldarelli, G., Heitmann, B., & Wilhelm, D. (2022). A deep ensemble learning method for automatic classification of multiplets in 1D NMR spectra [Conference poster]. In T. Prisner (Ed.), EUROMAR 2022 Abstractbook (p. 236). ZHAW Zürcher Hochschule für Angewandte Wissenschaften. https://doi.org/10.21256/zhaw-27328
• Schmid, N., Bruderer, S., Fischetti, G., Paruzzo, F., Toscano, G., Graf, D., Fey, M., Henrici, A., Grabner, H., Wegner, J. D., Sigel, R. K. O., Heitmann, B., & Wilhelm, D. (2022). Deconvolution of NMR spectra : a deep learning-based approach [Conference poster]. In T. Prisner (Ed.), EUROMAR 2022 Abstractbook (p. 242). ZHAW Zürcher Hochschule für Angewandte Wissenschaften. https://doi.org/10.21256/zhaw-27336

Weitere Publikationen

• Artificial Intelligence in Finance and Industry : Volume II - Highlights from the 7th European Conference. (2023). In A. Henrici, P. Schwendner, P. Deflorin, D. Wilhelm, & R. M. Füchslin (Eds.), 7th European COST Conference on Artificial Intelligence in Industry and Finance, Winterthur, Switzerland, 28 September 2022. Frontiers Research Foundation. https://www.frontiersin.org/research-topics/38909/
• Artificial Intelligence in Finance and Industry : highlights from 6 European COST conferences. (2022). In P. Deflorin, R. M. Füchslin, A. Henrici, J. Osterrieder, P. Schwendner, & D. Wilhelm (Eds.), 5th European COST Conference on Artificial Intelligence in Industry and Finance, Winterthur, Switzerland (online), 3 September 2020. Frontiers Research Foundation. https://www.frontiersin.org/research-topics/18514/
• Dep, R., Meier, B., Jenny, P., & Wilhelm, D. (2019). Stability analysis of gas bearing in Magic Angle Spinning (MAS) systems. 3rd Gas Bearing Workshop, Düsseldorf, Germany, 25 March 2019.
• Wilhelm, D. (2015). Rotating flow simulations with OpenFOAM. International Journal of Aeronautical Science & Aerospace Research, S1:001. https://doi.org/10.21256/zhaw-1441
• Wilhelm, D. (2015). Fluid flow dynamics in a Nuclear Magnetic Resonance system. Atlas of Science. http://atlasofscience.org/fluid-flow-dynamics/

Mündliche Konferenzbeiträge und Abstracts

Hostettler, M., Stingelin, S., De Lorenzi, F., Füchslin, R. M., Jacomet, C., Koll, S., Wilhelm, D., & Boiger, G. (2022). Modelling of peristaltic pumps for viscoelastic tube material properties under consideration of fatigue effects [Conference presentation]. Multiphysics 2022, 33. https://static1.squarespace.com/static/5c9f89c101232c1d41297d67/t/639912525a90a93394aa8b71/1670976084271/MULTIPHYSICS+2022-Abstract+Booklet.pdf

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