Skip to main contentSkip to search barSkip to navigationSkip to footer
Logo of the University of Applied Sciences and Arts Northwestern Switzerland
Degree Programmes
Continuing Education
Research and Services
International
About FHNW
DeEn
Locations and ContactFHNW LibraryMedia Relations
Logo of the University of Applied Sciences and Arts Northwestern Switzerland
  • Degree Programmes
  • Continuing Education
  • Research and Services
  • International
  • About FHNW
DeEn
Locations and ContactFHNW LibraryMedia Relations

Prof. Dr. Daniel Perruchoud

Daniel Perruchoud

Activities at FHNW

Lecturer for Data Science at the Institute for Data Science FHNW

Teaching

At Bachelor of Science in Data Science

  • Exploratory data analysis,
  • Applied in Machine Learning
  • Natural Language Processing
  • Supervisor of Bachelor project theses

At Master of Science in Engineering MSE

  • Advanced Natural Language Processing
  • Supervisor of Master students

Research

  • Natural Language Processing applications
  • AI-based information retrieval
    Machine Learning applications for process and product optimization

Profile

Projects

2025: Robust Swiss German Speech-to-Text System for Multi-Speaker Psychological Dialogues (in progress, Innosuisse funded)

2025: Low-latency Swiss German AI agents (in progress, Company funded)

2025: Artificial Intelligence Integration into the Safety Reporting System (in progress, Company funded)

2025: Schweizerdeutsch-to-LLM (in progress, Company funded)

2025: Robust visual identification of UI elements in business process testing (in progress, Innosuisse funded)

2025: Use of Natural Language Processing and Generative Artificial Intelligence for efficient information extraction (in progress, Company funded)

2025: Enhancing Patient Education with AI Avatars in Preoperative Care: Feasibility of German LLMs (Innosuisse funded)

2025: LUKE App for visual cloth discrimination (Innosuisse funded)

2025: Optimization of the production process of current and voltage transformers with machine learning (Company & HTZ funded)

2025: AI-based Solutions for FtO Assessment in Technical Patent Searches (Company & HTZ funded)

2024: “AI Tutor for Students” - Efficient Information Extraction with Retrieval Augmented Generation (FHNW funded)

2024: “RockPulse” - Sensing precursors of landslide accelerations in ambient vibration data (Innosuisse funded)

2024: “AI-Selfie@ENTER” - Developing personalized selfies with generative AI (FHNW funded)

2024: Understanding Client Sentiment with NLP from Small Data (Company & HTZ funded)

2024: Introduce data-driven development and operation in device qualification (Innosuisse funded)

2022: Enhancing DSM’s Third Party Patent Monitoring with Machine Learning and Natural Language Processing (Company funded)

2021: ML-based visual quality monitoring for Hybrid Photon Counting device manufacturing (Innosuisse funded)

2020: Condition Monitoring and Predictive Maintenance for Wind Turbines Developed on Massive Wind Park Sensor Data (Innosuisse funded)

Biography

1986-1991

Diploma in theoretical solid state physics (dipl. phys. ETH Zürich, Switzerland)

1992-1996

PhD in the domain of climate change impact studies (Dr. sc. nat. ETH Zürich, Switzerland)

1997-1999

Research Associate at the Swiss Federal Office for the Environment

1999-2000

Analytical Consultant at NCR / Teradata Wallisellen, Switzerland

2000-2008

Data Scientist at UBS Switzerland AG Zürich, Switzerland

2009-2018

Senior Data Scientist and Team Lead at UBS Switzerland AG Zürich, Switzerland

Since 2019

Professor at FHNW in Brugg-Windisch, Switzerland

LinkedIn

Prof. Dr. Daniel Perruchoud

Prof. Dr. Daniel Perruchoud

Lecturer for Data Science

Telephone

+41 56 202 83 41 (undefined)

E-mail

daniel.perruchoud@fhnw.ch

Address

FHNW University of Applied Sciences and Arts Northwestern Switzerland School of Computer Science Bahnhofstrasse 6 CH-5210 Windisch

ht_ins_i4ds_mitarbeitende

What we offer

  • Degree Programmes
  • Continuing Education
  • Research and Services

About FHNW

  • Schools
  • Organisation
  • Management
  • Facts and Figures

Information

  • Data Protection
  • Accessibility
  • Imprint

Support & Intranet

  • IT Support
  • Login Inside-FHNW

Member of: