About
Andreas Brink‑Kjær is a Tenure Track Assistant Professor in the Digital Health section of DTU's Department of Health Technology. His research develops computational methods — signal processing, deep learning, and foundation models — that turn physiological signals such as sleep recordings and wrist accelerometry into scalable, clinically meaningful measures of health and disease.
He completed his PhD in 2022 at DTU, with research conducted across DTU and Stanford University's Center for Sleep Sciences and Medicine, following an MSc in Biomedical Engineering (DTU, 2018) that included a research stay at Stanford. He was a postdoctoral researcher at DTU from 2022–2024 before starting his current position in 2025.
Contact
Background
Positions
2017–2018
Visiting Student Researcher
Stanford University, Center for Sleep Sciences and Medicine
Research stay during MSc studies.
2025–present
Tenure Track Assistant Professor
Technical University of Denmark (DTU)
2019
Research Assistant
Danish Center for Sleep Medicine, Glostrup Hospital / Rigshospitalet
2022–2024
Postdoctoral Researcher
Technical University of Denmark (DTU)
2019–2022
PhD Student
Technical University of Denmark (DTU)
PhD research included substantial activity at Stanford University.
2017
Trainee
Pharma IT / ALK
Contributed to pharmacovigilance safety operations at ALK.
Education
2022
PhD, Biomedical Engineering — physiological signal analysis and machine learning
Technical University of Denmark (DTU)
Thesis: Design of Interpretable End-to-End Deep Learning Models for Diagnosis of Sleep Disorders and Sleep Quality Evaluation
Research conducted across DTU and Stanford University, Center for Sleep Sciences and Medicine.
2018
MSc, Biomedical Engineering
Technical University of Denmark (DTU)
Teaching
Design-build 4: Autonomous devices for controlling and studying living systems (22400)
Introduction to Biomedical Engineering (22460)
Wearable Sensors: Designing and Prototyping (22058)
Introduction to Medical Data Science (22062)
Supervision & mentoring record
23 completed supervised projects since 2020, spanning PhD co-supervision through BSc special courses. Student names and grades are not published; see the Opportunities page for how to get involved.
PhD · 2 completed
Deep Learning Analysis of Normal and Disordered Sleep using Physiological Signals
Advanced signal processing and deep learning framework for discovering molecular underpinnings
MSc · 18 completed
AI-Based Screening for Narcolepsy Using Wrist-Worn Wearables
Automatic Detection of Central Hypersomnias Using Sleep Signals and Machine Learning
How the heart chambers crosstalk – an electrocardiographic study in patients with prior myocardial infarction
Development and Validation of Edge-Based Bio-Signal Processing for Wearable Sensors
Adapting Large Language Models for Structured Information Extraction from Sleep Study Reports
Impact of Obstructive Sleep Apnea on Cardiac Electrophysiology and Cardiac Disease Development
Heart–Lung–Brain Coupling as a Digital Biomarker in RBD
Heart-Brain Interaction Biomarkers in Sleep for Detection of REM Sleep Behavior Disorder
Automatic Detection of Microsleep Events in Daytime EEG Using Deep Learning
Latent modeling of acetylcholine dynamics during sleep under warming and cholinesterase inhibition
Sleep Analysis from Wrist Accelerometer Using Contrastive Deep Learning
Cardiovascular impact of obstructive sleep apnea
Characterization of Abnormal Eye Movements in Sleep for Profiling of Neurodegenerative Disease
Brain Coherence Based Profiling of Neurological Disorders
Characterizing Microsleep in Patients with Narcolepsy and Other Central Hypersomnias
Frequency content of EEG and EOG for mortality prediction
A new deep learning approach for characterization of spectral density changes in alpha-synuclein associated neurodegeneration
Advanced analysis and interpretation of nocturnal instability and spontaneous arousability in insomnia patients
BSc · 3 completed
Scientific Writing and Advanced Analysis of Hypnodensity Data in Sleep Research
EOG-Only Digital Biomarkers for REM Sleep Behavior Disorder and Parkinson’s Disease
Learning Sleep Disorder Signatures from Hypnodensity Representations in a Large Polysomnography Cohort
Funding, awards & collaborations
Major collaborations
Danish Center for Sleep Medicine, Rigshospitalet
Stanford University, Center for Sleep Sciences and Medicine
Icahn School of Medicine at Mount Sinai
International RBD Study Group and multicenter partners
Funding
Rebalancing sleep-wake disturbances in Parkinson's disease with deep brain stimulation
Automated actigraphy analysis for identification of prodromal Parkinson disease or related disorders
Awards
Best Oral Abstract Award