Postdoc Position in Privacy-aware Federated Machine Learning for Clinical Data REANNOUNCEMENT
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Odense M
At the Department of Mathematics and Computer Science at the University of Southern Denmark, the Computational Biology group chaired by Associate Professor Richard Röttger invites applications for a postdoctoral position for the creation of a federated machine learning infrastructure for sensitive clinical data to be filled as soon as possible. The position will initially be for a period of one year, extendable for up to three years.
A large roadblock of medical research is the difficult access to sensitive data and therefore hinders the training of complex and powerful machine learning concepts. This issue is amplified when considering rare diseases with low incidence numbers per hospital. The aim is to utilize the scattered data across Europe and make these data treasures amenable for machine learning.
To that end, we will design and implement a federated metadata repository which allows the identification and selection of suitable patients across the data site without the need of ever transferring sensitive information. Federated algorithms can subsequently be trained on the defined cohorts, again without sensitive data leaving the safe harbour of storage. Additionally, it is required ensure the compatibility and interoperability of the data at the different sites by developing complex data homogenization and standardization pipelines which can flexibly be deployed and adjusted to the requirements of the data holding sites.
The successful candidate will be part of a large European endeavour Screen4Care and work in a dynamic, international, and interdisciplinary environment. Screen4Care is funded by the Innovative Medicines Initiative in a public/private partnership between numerous, high-profile research institutes and the EFPIA. The overall vision of Screen4Care is to shorten the path to rare disease diagnosis; the envisioned federated algorithms and infrastructure is one key aspect of fulfilling this vision.
Within SDU, the candidate will be embedded in a highly international and powerful consortium as a member of the Computational Biology group which offers a wide expertise in machine learning of biomedical data, both in the realm of unsupervised and supervised learning. We also provide the candidate with direct access to state-of-the-art computing facilities optimized for deep learning and distributed computing.
We are seeking outstanding candidates with strong analytical and problem solving skills, who are strong in written and oral communication (in English), and have documented experience in the development and maintenance of compute infrastructures and data homogenization, preferably in the area of clinical science. Expertise in data science and machine learning, as well as computer security and data privacy, are welcome.
Place of work:
Computational Biology Group (Associate Professor Richard Röttger, [email protected]), Department of Mathematics and Computer Science, located at the main campus of the University of Southern Denmark, Odense, Denmark.
This position is subject to the entry into force of grant agreement no 101034427 by and between the Innovative Medicines Initiative 2 Joint Undertaking (JU) and SDU and thereby funded by the European Union’s Horizon 2020 Research and Innovation Programme.
For further information please contact Associate Professor Richard Röttger, e-mail: [email protected].
Application, salary etc.
The successful applicant will be employed in accordance with the agreement between the Ministry of Finance and AC (the Danish Confederation of Professional Associations). Please check links for more information on salary and taxation.
Application must be made in the form of a Declaration of Interest and must include the following documents:
• Letter of motivation, including details on qualifications within subject area (max. one page).
• A curriculum vitae including information on previous employment, list of programming skills mastered by the applicant, and teaching and supervision experience.
• A full list of publications stating the scientific publications on which the applicant wishes to rely (if available).
• A certified copy of your PhD degree certificate
• At least one letter of recommendation from supervisors or previous employers.
To qualify you must have passed a PhD or equivalent. Applications will be assessed by an expert assessor/committee. Applicants will be informed of their assessment by the university.
The University wishes our staff to reflect the diversity of society and thus welcomes applications from all qualified candidates regardless of personal background.
Applications must be submitted electronically using the link "Apply online. Attached files must be in Adobe PDF or Word format. Each box can only contain a single file of max. 10 Mb. We strongly recommend that you read How to apply before you apply.
Further information for international applicants considering applying for a job at the University of Southern Denmark. Please take a look at the webpage from our International Staff Office (ISO) at The University of Southern Denmark.
A large roadblock of medical research is the difficult access to sensitive data and therefore hinders the training of complex and powerful machine learning concepts. This issue is amplified when considering rare diseases with low incidence numbers per hospital. The aim is to utilize the scattered data across Europe and make these data treasures amenable for machine learning.
To that end, we will design and implement a federated metadata repository which allows the identification and selection of suitable patients across the data site without the need of ever transferring sensitive information. Federated algorithms can subsequently be trained on the defined cohorts, again without sensitive data leaving the safe harbour of storage. Additionally, it is required ensure the compatibility and interoperability of the data at the different sites by developing complex data homogenization and standardization pipelines which can flexibly be deployed and adjusted to the requirements of the data holding sites.
The successful candidate will be part of a large European endeavour Screen4Care and work in a dynamic, international, and interdisciplinary environment. Screen4Care is funded by the Innovative Medicines Initiative in a public/private partnership between numerous, high-profile research institutes and the EFPIA. The overall vision of Screen4Care is to shorten the path to rare disease diagnosis; the envisioned federated algorithms and infrastructure is one key aspect of fulfilling this vision.
Within SDU, the candidate will be embedded in a highly international and powerful consortium as a member of the Computational Biology group which offers a wide expertise in machine learning of biomedical data, both in the realm of unsupervised and supervised learning. We also provide the candidate with direct access to state-of-the-art computing facilities optimized for deep learning and distributed computing.
We are seeking outstanding candidates with strong analytical and problem solving skills, who are strong in written and oral communication (in English), and have documented experience in the development and maintenance of compute infrastructures and data homogenization, preferably in the area of clinical science. Expertise in data science and machine learning, as well as computer security and data privacy, are welcome.
Place of work:
Computational Biology Group (Associate Professor Richard Röttger, [email protected]), Department of Mathematics and Computer Science, located at the main campus of the University of Southern Denmark, Odense, Denmark.
This position is subject to the entry into force of grant agreement no 101034427 by and between the Innovative Medicines Initiative 2 Joint Undertaking (JU) and SDU and thereby funded by the European Union’s Horizon 2020 Research and Innovation Programme.
For further information please contact Associate Professor Richard Röttger, e-mail: [email protected].
Application, salary etc.
The successful applicant will be employed in accordance with the agreement between the Ministry of Finance and AC (the Danish Confederation of Professional Associations). Please check links for more information on salary and taxation.
Application must be made in the form of a Declaration of Interest and must include the following documents:
• Letter of motivation, including details on qualifications within subject area (max. one page).
• A curriculum vitae including information on previous employment, list of programming skills mastered by the applicant, and teaching and supervision experience.
• A full list of publications stating the scientific publications on which the applicant wishes to rely (if available).
• A certified copy of your PhD degree certificate
• At least one letter of recommendation from supervisors or previous employers.
To qualify you must have passed a PhD or equivalent. Applications will be assessed by an expert assessor/committee. Applicants will be informed of their assessment by the university.
The University wishes our staff to reflect the diversity of society and thus welcomes applications from all qualified candidates regardless of personal background.
Applications must be submitted electronically using the link "Apply online. Attached files must be in Adobe PDF or Word format. Each box can only contain a single file of max. 10 Mb. We strongly recommend that you read How to apply before you apply.
Further information for international applicants considering applying for a job at the University of Southern Denmark. Please take a look at the webpage from our International Staff Office (ISO) at The University of Southern Denmark.
Information og data
Denne ledige stilling har jobtypen "Forsker", og befinder sig i kategorien "Sundhed og forskning".
Arbejdsstedet er beliggende i Odense M.
Jobbet er oprettet på vores service den 3.11.2021, men kan have været deaktiveret og genaktiveret igen.
Dagligt opdateret: Dette job opdateres dagligt ud fra jobudbyderens hjemmeside via vores søgemaskineteknologi og er aktivt lige nu.
- Forsker
- Odense M
- Søndag den 12. december 2021
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