PhD scholarship in Sleep Analytics Applied to Genomics and Proteomics

The Department of Health Technology (DTU Health Tech) invites applicants for a 3-year PhD in sleep analytics and biological data sciences. 

The goal will be to design and use bioinformatic pipelines, biomedical signal processing and machine learning algorithms for the study of sleep disorders in conjunction with large scale biological (genetic and proteomic data) and clinical (questionnaire, diagnosis, outcome) variables.  The goal is to increase our molecular understanding of specific sleep mechanism (for example REM sleep) and sleep disorders (for example sleep walking, restless leg syndrome).  As an example, we have designed supervised machine learning to automatically score sleep stages or sleep events in nocturnal polysomnography (PSG) signals, and these can be correlated with Genome Wide Association (GWAS) or targeted whole genome sequence (WGS) data in tens of thousands of individuals to examine the genetics of REM sleep.  Subjective data on REM related symptoms with GWAS are similarly available through the UK biobank, U Copenhagen data and in Stanford’s database of patients that evaluate symptoms related to REM sleep such as sleep paralysis, nightmare etc. using a similar questionnaire.  Similarly, we have designed multivariate models using blood proteomic data (5,500 proteins) to predict circadian phase and aim at using more complex, non- linear methodologies to improve phase prediction and correlate these findings with genomic, PSG and other data.  Half of the 3 years will be spent at DTU and Rigshospitalet, and half at Stanford University in California, USA. The PhD is part of a longstanding collaboration between these Universities for this topic. Three professors, see below, with experience in technical science, data science and sleep medicine will supervise closely the student in both locations.  

Responsibilities
The ultimate purpose of the PhD project is to link genetic information (for ex. GWAS, WGS) with blood biomarkers (proteomics), sleep signals (for example REM sleep or Periodic Leg Movements during Sleep), and subjective sleep symptoms (for example sleep paralysis or Restless Leg Syndrome).  In this position, you can expect to perform data analysis on tens of thousands of laboratories recorded sleep studies, in combination with large scale genetic, proteomic, and medical/questionnaire data. One data type will be sleep study data, or polysomnography (PSG). A PSG is comprised of multiple physiological signals (electroencephalography, electrocardiography, electrooculography, chin, and leg electromyography, respiratory, oxygen saturation, etc.) recorded simultaneously throughout the night to provide physiological measures of human activity and behavior during sleep. Another data type will be GWAS. It contains data on millions of Single Nucleotide Polymorphic markers in every individual.  Proteomic data include measurements of 1-7,000 proteins in each subject. Finally questionnaire data involves answers to 172 sleep and health history symptoms questions with branching logic in each individual.  You will be expected to transform and analyze digital signals by innovating biomedical signal processing methods as well as innovative machine learning and advanced deep learning techniques (discriminative-, generative-, hybrid- and self-learning deep learning techniques for deep learning neural networks), and will use advance statistics in the analysis of the biological and questionnaire data.

The PhD student will work in a highly collaborative environment and develop novel algorithms for processing PSG and genetic data based on advanced interpretable machine learning techniques to describe and evaluate sleep quality. Collaborations across other labs and across departments are encouraged. Experience in advanced biomedical signal processing, programming, advanced mathematics, and a solid background in statistical analysis are needed. Excellent interpersonal skills and the ability to interact effectively with members of the research teams are essential to the success of the individual in this position. The successful candidate must be able to learn and work independently yet collaborate effectively with co-workers.

Qualifications elaborated:

• A two-year master's degree (120 ECTS points) or a similar degree with an academic level equivalent to a two-year master's degree.
• MSc in biomedical engineering or electrical engineering, biomedical data science or equivalent qualification with preferably publication record
• Understanding of biological principles, neurosciences, or genetics is a plus.  Interest in learning about these area is needed.
• Excellent collaborative skills
• Strong knowledge and experience of advanced biomedical signal processing methods and algorithms
• Excellent command of English (written and spoken) as well as technical writing.
• An understanding of advanced mathematical & statistical principles behind current best practices in high-throughput data analysis.
• Strong experience in the use of a high-level programming languages such as phyton, Matlab and similar for complex signal/data analysis.
• Preferably familiarity with high performance computing and computing clusters.
• Ability and willingness to mentor students.
• Ability to provide advice to lab members on appropriate data analysis approaches.
• Ability to work both independently and collaboratively in complex organizations (technical/medical), and to handle several concurrent projects.
• Exceptionally strong communication and interpersonal skills.
• Excellent data presentation and visualization skills.
• Ability to effectively present complex results in a clear and concise manner that is accessible to a diverse audience.
• Enthusiasm for learning more.

Approval and Enrolment
The scholarship for the PhD degree is subject to academic approval, and the candidate will be enrolled in one of the general degree programmes at DTU. For information about our enrolment requirements and the general planning of the PhD study programme, please see DTU's rules for the PhD education . 

Assessment
The assessment of the applicants will be made by Assoc. Professor Helge B.D. Sørensen (DTU Health Tech)/chairman, Professor Poul Jennum (Rigshospitalet CPH University), and Professor Emmanuel Mignot (Stanford University).

We offer
DTU is a leading technical university globally recognized for the excellence of its research, education, innovation and scientific advice. We offer a rewarding and challenging job in an international environment. We strive for academic excellence in an environment characterized by collegial respect and academic freedom tempered by responsibility.

Salary and appointment terms
The appointment will be based on the collective agreement with the Danish Confederation of Professional Associations. The allowance will be agreed upon with the relevant union. The period of employment is 3 years.

Workplace

• Digital Health Section, Department of Health Technology, Technical University of Denmark, Rigshospitalet, and Stanford University, USA
• The candidate will be enrolled as a PhD student at DTU Health Tech but is required to spend up to 50% of the time at Stanford University outside Denmark.

You can read more about career paths at DTU here . 

Further information
Further information may be obtained from Assoc. Professor Ph.D. Helge B.D. Sørensen, [email protected], Biomedical Signal Processing & AI Group, Digital Health Section.

You can read more about DTU Health Tech at www.healthtech.dtu.dk/english .

If you are applying from abroad, you may find useful information on working in Denmark and at DTU at DTU – Moving to Denmark .

Application procedure
Your complete online application must be submitted no later than28 January 2022 (Danish time). Applications must be submitted as one PDF file containing all materials to be given consideration. To apply, please open the link "Apply online", fill out the online application form, and attach all your materials in English in one PDF file. The file must include:

• A letter motivating the application (cover letter)
• Curriculum vitae
• Grade transcripts and BSc/MSc diploma including official description of grading scale

You may apply prior to ob­tai­ning your master's degree but cannot begin before having received it.

Applications received after the deadline will not be considered.

All interested candidates irrespective of age, gender, race, disability, religion or ethnic background are encouraged to apply.

DTU Health Tech engages in research, education, and innovation base on technical and natural science for the healthcare sector. The Healthcare sector is a globally expanding market with demands for the most advanced technological solutions. DTU Health Tech creates the foundation for companies to develop new and innovative services and products which benefit people and create value for society. DTU Health Techs expertise spans from imaging and biosensor techniques, across digital health and biological modelling, to biopharma technologies. The department has a scientific staff of about 210 persons, 130 PhD students and a technical/administrative support staff of about 160 persons, of which a large majority contributes to our research infrastructure and related commercial activities.

Technology for people
DTU develops technology for people. With our international elite research and study programmes, we are helping to create a better world and to solve the global challenges formulated in the UN’s 17 Sustainable Development Goals. Hans Christian Ørsted founded DTU in 1829 with a clear vision to develop and create value using science and engineering to benefit society. That vision lives on today. DTU has 12,900 students and 6,000 employees. We work in an international atmosphere and have an inclusive, evolving, and informal working environment. DTU has campuses in all parts of Denmark and in Greenland, and we collaborate with the best universities around the world.