Nicolas Schmid
Nicolas Schmid
ZHAW
School of Engineering
Forschungsschwerpunkt Applied Complex Systems Science
Technikumstrasse 71
8400 Winterthur
Projects
- NMR-based drug discovery / Team member / Project ongoing
- Machine learning for NMR spectroscopy / Team member / Project completed
- Epidemiologische Multiskalensimulation zur Analyse der Übertragungsmechanismen in der COVID-19 Pandemie / Team member / Project completed
- Feasibility Study Reinforcement Learning for Heating Systems / Team member / Project completed
Publications
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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).
Available from: https://doi.org/10.1016/j.jmr.2022.107357
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Fischetti, Giulia; Schmid, Nicolas; Bruderer, Simon; Caldarelli, Guido; Scarso, Alessandro; Henrici, Andreas; Wilhelm, Dirk,
2023.
Automatic classification of signal regions in 1H nuclear magnetic resonance spectra.
Frontiers in Artificial Intelligence.
5(1116416).
Available from: https://doi.org/10.3389/frai.2022.1116416
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Bolt, Peter; Ziebart, Volker; Jaeger, Christian; Schmid, Nicolas; Stadelmann, Thilo; Füchslin, Rudolf Marcel,
2024.
A simulation study on energy optimization in building control with reinforcement learning [paper].
In:
11th IAPR TC 3 Workshop on Artificial Neural Networks for Pattern Recognition (ANNPR'24), Montreal, Canada, 10-12 October 2024.
ZHAW Zürcher Hochschule für Angewandte Wissenschaften.
Available from: https://doi.org/10.21256/zhaw-31129
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Schmid, Nicolas; Wanner, Marc; Fischetti, Giulia; Meshkian, Mohsen; Bruderer, Simon; Henrici, Andreas; Wegner, Jan Dirk; Sigel, Roland K.O.; Heitmann, Björn; Wilhelm, Dirk,
2024.
Automated spin system analysis in NMR spectroscopy with SpinDETR : a deep learning approach [poster].
In:
Datalab-Symposium, Winterthur, Schweiz, 12. September 2024.
Winterthur:
ZHAW Zurich University of Applied Sciences.
Available from: https://doi.org/10.21256/zhaw-31443
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Fischetti, Giulia; Schmid, Nicolas; Bruderer, Simon; Henrici, Andreas; Heitmann, Björn; Scarso, Alessandro; Caldarelli, Guido; Wilhelm, Dirk,
2024.
MuSe Net: a deep learning framework for trustworthy multiplet segmentation in 1D 1H NMR spectra [poster].
In:
Datalab-Symposium, Winterthur, Schweiz, 12. September 2024.
Winterthur:
ZHAW Zurich University of Applied Sciences.
Available from: https://doi.org/10.21256/zhaw-31444
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Schmid, Nicolas; Wanner, Marc; Fischetti, Giulia; Meshkian, Mohsen; Bruderer, Simon; Henrici, Andreas; Wegner, Jan Dirk; Sigel, Roland K. O.; Heitmann, Bjoern; Wilhelm, Dirk,
2024.
Automated spin system analysis in NMR spectroscopy with SpinDETR : a deep learning approach [poster].
In:
20th European Magnetic Resonance Congress (EUROMAR), Bilbao, Spain, 30 June - 4 July 2024.
ZHAW Zürcher Hochschule für Angewandte Wissenschaften.
Available from: https://doi.org/10.21256/zhaw-31101
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Fischetti, Giulia; Schmid, Nicolas; Bruderer, Simon; Henrici, Andreas; Heitmann, Bjoern; Scarso, Alessandro; Caldarelli, Guido; Wilhelm, Dirk,
2024.
MuSe Net : a deep learning framework for trustworthy multiplet segmentation in 1D 1H NMR spectra [poster].
In:
20th European Magnetic Resonance Congress (EUROMAR), Bilbao, Spain, 30 June - 4 July 2024.
ZHAW Zürcher Hochschule für Angewandte Wissenschaften.
Available from: https://doi.org/10.21256/zhaw-31102
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Schmid, Nicolas; Fischetti, Giulia; Henrici, Andreas; Wilhelm, Dirk; Wanner, Marc; Meshkian, Mohsen; Bruderer, Simon; Wegner, Jan-Dirk; Sigel, Roland K. O.; Heitmann, Bjoern; Konukoglu, Ender,
2023.
Transforming NMR spectroscopy : extraction of multiplet parameters with deep learning [poster].
In:
Prisner, Thomas, ed.,
Euromar 2022 Abstractbook.
European Conference on Magnetic Resonance (EUROMAR), Utrecht, The Netherlands, 10-14 July 2022.
ZHAW Zürcher Hochschule für Angewandte Wissenschaften.
pp. 291.
Available from: https://doi.org/10.21256/zhaw-29510
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Fischetti, Giulia; Schmid, Nicolas; Henrici, Andreas; Wilhelm, Dirk; Bruderer, Simon; Heitmann, Bjoern; Scarso, Alessandro; Caldarelli, Guido,
2023.
Uncertainty quantification for reliable automatic multiplet classification in 1H NMR spectra [poster].
In:
Prisner, Thomas, ed.,
Euromar 2023 Programme & Abstract Book.
European Conference on Magnetic Resonance (EUROMAR), Glasgow, United Kingdom, 9-13 July 2023.
ZHAW Zürcher Hochschule für Angewandte Wissenschaften.
pp. 350.
Available from: https://doi.org/10.21256/zhaw-29538
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Schmid, Nicolas; Bruderer, Simon; Fischetti, Giulia; Paruzzo, Federico; Toscano, Giuseppe; Graf, Dominik; Fey, Michael; Ziebart, Volker; Henrici, Andreas; Grabner, Helmut; Wegner, Jan Dirk; Sigel, Roland K.O.; Heitmann, Björn; Wilhelm, Dirk,
2023.
Deconvolution of NMR spectra : a deep learning-based approach [poster].
In:
Datalab Symposium, Winterthur, Schweiz, 11. Januar 2023.
ZHAW Zürcher Hochschule für Angewandte Wissenschaften.
Available from: https://doi.org/10.21256/zhaw-27429
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Fischetti, Giulia; Schmid, Nicolas; Bruderer, Simon; Paruzzo, Federico; Toscano, Giuseppe; Graf, Dominik; Fey, Michael; Henrici, Andreas; Scarso, Alessandro; Caldarelli, Guido; Heitmann, Björn; Wilhelm, Dirk,
2022.
A deep ensemble learning method for automatic classification of multiplets in 1D NMR spectra [poster].
In:
Prisner, Thomas, ed.,
EUROMAR 2022 Abstractbook.
European Conference on Magnetic Resonance (EUROMAR), Utrecht, The Netherlands, 10-14 July 2022.
ZHAW Zürcher Hochschule für Angewandte Wissenschaften.
pp. 236.
Available from: https://doi.org/10.21256/zhaw-27328
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Schmid, Nicolas; Bruderer, Simon; Fischetti, Giulia; Paruzzo, Federico; Toscano, Giuseppe; Graf, Dominik; Fey, Michael; Henrici, Andreas; Grabner, Helmut; Wegner, Jan Dirk; Sigel, Roland K. O.; Heitmann, Björn; Wilhelm, Dirk,
2022.
Deconvolution of NMR spectra : a deep learning-based approach [poster].
In:
Prisner, Thomas, ed.,
EUROMAR 2022 Abstractbook.
European Conference on Magnetic Resonance (EUROMAR), Utrecht, The Netherlands, 10-14 July 2022.
ZHAW Zürcher Hochschule für Angewandte Wissenschaften.
pp. 242.
Available from: https://doi.org/10.21256/zhaw-27336