Image, Video and Multidimensional Signal Processing

We are looking for candidates interested in developing deep learning algorithms for brain-related 2D/3D MRI or microscopy images. Possible topics include image registration, segmentation or the automatic tracking/tracing of neurons in large 3D image data. We also welcome new project proposals related to brain/neuron image data analysis.

Experience in biomedical image analysis is a plus, but not a requirement. This position can be a great opportunity to apply knowledge and expertise from computer vision and/or image processing to new problems in the biomedical field.

Our webpage can be found here: https://bia.riken.jp We are looking forward to hearing from you

Postdoctoral Fellow, Research Scientist, and Instructor in Biomedical Optics and Medical Physics

We are looking for skilled and enthusiastic candidates to fill Postdoctoral Fellow, Research Scientist, and Instructor positions in the Biomedical Imaging and Radiation Technology Laboratory(BIRTLab) at Department of Radiation Oncology in UT Southwestern Medical Center. Our mission is to innovate, develop, and apply biomedical technology to empower cancer research.

Successful candidates will be joining our team to work either one of the following projects:

(1).       Establishing an ultra-sensitive optical imaging integrated with X-ray cone beam CT(CBCT)/MRI imaging for in vivo tumor and immune cell tracking to facilitate cancer therapy development. The research involves imaging reconstruction, and the development of camera-lens imaging system or single pixel imaging.

(2).       Developing optical tomography-guided system for pre-clinical radiation therapy research; specifically, we will develop a fluorescence, bioluminescence, and diffuse optical tomography system to localize tumors in vivo, guide irradiation, and quantify treatment response.

(3).       Establishing molecular image-guided system for ultra-high-dose rate (FLASH) radiation therapy as a new cancer treatment paradigm; the research involves numerical oxygen transport modeling, Monte-Carlo simulation, optical imaging and radiation physics to assess the efficacy of FLASH therapy.

The projects are multi-disciplinary and integrate engineering, algorithm development, optics, radiation physics, biology, and industrial components. Success completion of the projects will significantly advance image-guided systems and radiation technology to improve cancer treatment.

BIRTLab provides an outstanding environment to grow candidates toward successful careers.

• PI Dr. Wang works tirelessly with candidates to ensure they meet their career goals. Through attentive guidance, he encourages members to think creatively and develop their own research projects. All activities are supported by extramural funding through the NIH and Texas CPRIT.
• Successful members are also eligible for basic clinical medical physics training and a tuition fee waiver to enroll in a certificate program with CAMPEP-accredited courses, which covers medical physics didactic elements for people who enter the medical physics profession through an alternative pathway.

Multi-disciplinary projects, a strong research environment, and the medical physics pathway together provide a unique opportunity to prepare the candidate for careers in academia and industry, or to become a professional medical physicist in the U.S.

Candidates with established experience in numerical algorithm development, biomedical optics, or engineering system design and development are desired. Candidates who hold degrees in mathematics, physics, biomedical engineering, optics, computer science and engineering are encouraged to apply. Further details about the BIRTLab and projects can be found at https://www.utsouthwestern.edu/labs/birt/

Position and compensation are based on candidates’ experience and NIH scale with highly competitive benefits. UT Southwestern Medical Center is in Dallas, Texas. Dallas is the fourth-largest metropolitan area in the US with fast growing industrial sectors and job opportunities. Interested candidates should send a statement of interest, CV, and the contact of 3 references to:

Ken Kang-Hsin Wang, Ph.D., DABR

Associate Professor

CPRIT Scholar in Cancer Research

Division of Medical Physics and Engineering

Department of Radiation Oncology

UT Southwestern Medical Center

Kang-Hsin.Wang@utsouthwestern.edu

Positions: The Gordon Center for Medical Imaging (GCMI) in the Department of Radiology at Massachusetts General Hospital (MGH) and Harvard Medical School (HMS) has multiple openings for highly qualified individuals at the postdoctoral level to work with Prof. Kuang Gong on NIH funded projects related to PET image reconstruction, medical image analysis, and machine learning methodologies.

The research projects aim to utilize machine learning-based image reconstruction and analysis to improve the diagnosis and progression tracking of Alzheimer’s disease (AD) and cancer. The projects are based on collaborations with clinicians from MGH, Harvard Aging Brain Study (HABS) and MD Anderson Cancer Center (MDACC). The successful candidate will have joint appointments at MGH and HMS.

Requirements:

·      Applicants should have earned a Ph.D. in engineering, statistics, mathematics, physics, neuroscience, or a related field.

·      Strong analytical, quantitative, programming and communication skills are essential.

·      Prior research experience with image reconstruction or medical image analysis is required.

·      Prior research experience with machine learning or deep learning, and proficiency in Pytorch/TensorFlow programming is desirable.

·      Applicants should be self-motivated and able to work independently as well as in a collaborative environment.

Environment: The Department of Radiology offers extensive core research facilities, including a new digital time-of-flight PET/CT scanner, brain and whole-body PET/MRI scanners, small animal PET/SPECT/CT systems and several MRI scanners, including two 7T ultra-high-field scanners. It also includes a large-scale computing facility for image analysis, network training, tomographic reconstruction, Monte Carlo simulation, and other computationally intensive research applications. The successful applicant can interact collaboratively with a large, growing research group in diverse areas of imaging technology and applications.

Apply: The positions are available as of June 2022 and the start date is flexible. If interested, please send your curriculum vitae, a cover letter describing your background and research interests, and contact information of three references to Prof. Gong (kgong@mgh.harvard.edu). Applications will be reviewed in a rolling basis until the positions are filled.

We are seeking a highly motivated and skilled PhD student to work with us on the development of a digital phenotyping strategy for children with autism. The PhD student will use machine learning approaches to provide automated measures of body movement and social scenes for children with autism, with the goal to support automated autism diagnosis and/or fine-grained characterization of autistic symptoms. Strong skills in computer vision, geometric modeling of 3D scenes featuring human bodies and objects (static or in motion), applied machine learning, as well as interests in advanced machine learning are an asset.

Job description: https://schaerm.github.io/AutismBrainBehavior/blog/2022/PhD_position_opening/

Review of applications will start immediately and continue until the position is filled.

The Department of Electrical Engineering at Stanford University (http://ee.stanford.edu/) invites applications for a tenure-track faculty appointment at the junior level (Assistant or untenured Associate Professor) in the broadly defined field of electrical and computer engineering. Priority will be given to the overall originality and promise of the candidate’s work over any specific area of specialization.

Applicants should have an earned Ph.D., evidence of the ability to pursue an independent program of research, a strong commitment to both graduate and undergraduate teaching, and the ability to initiate and conduct research across disciplines. A successful candidate will be expected to teach courses at the graduate and undergraduate levels and to build and lead a team of graduate students in Ph.D. research.

Applications should include a brief research and teaching plan, a detailed resume including a publications list, and the names and email addresses of at least five references.

The Electrical Engineering Department, School of Engineering, and Stanford University value faculty who are committed to advancing diversity, equity, and inclusion. Candidates may optionally include as part of their research or teaching statement a brief discussion of how their work will further these ideals.

Candidates should apply online at http://ee.stanford.edu/job-openings. Applications will be accepted through November 28, 2021.

Stanford is an equal employment opportunity and affirmative action employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other characteristic protected by law. Stanford also welcomes applications from others who would bring additional dimensions to the University's research, teaching and clinical missions.

2 x Professor / Reader of Machine Learning and Artificial Intelligence

Department of Computer Science
University of Surrey
Guildford, UK

https://jobs.surrey.ac.uk/037121 

The Department of Computer Science at the University of Surrey seeks to recruit two research leaders with an international profile and an outstanding research and publication track record to lead a substantial and sustained portfolio of research in any of the following areas within Machine Learning (ML) and Artificial Intelligence (AI):

• Trustworthy AI (explainable, secure and privacy preserving machine learning)
• Deep learning for natural language processing
• Computational neuroscience and machine learning
• Distributed machine learning and optimization
• AI planning and optimal control

The Department of Computer Science has a world-class reputation in these areas and regularly publishes at top-level conferences and journals. The Department is home to the Nature Inspired Computing and Engineering (NICE) group. The group has 10 academics and holds world-leading expertise in evolutionary computation, computational neuroscience, autonomous and self-organising systems, reinforcement learning, Bayesian learning, privacy-preserving machine learning, explainable AI, computer vision, and image and natural language processing. The research group maintains close links with leading industries, the public sector, and governmental bodies, leading to a strong heritage of real-world impact.

The NICE group is a key partner in the new Surrey Institute for People-Centred AI, a major strategic investment bringing together world-leading expertise in fundamental AI theory with cross-university domain expertise to realise and shape AI impact for public good. It is expected that the postholders will play a leading role in this new Institute.

Each successful candidate will have proven success in leading multi-Faculty research proposals and securing funding through collaborative group bids. One postholder will also be considered for the strategic leadership role of the Head of the NICE group. Each postholder will also take an active role in training the ML scientists of the future by leading teaching activities at both undergraduate and postgraduate level within the University’s School of Computer Science and Electronic Engineering.

These are full-time and permanent positions. The postholders will benefit from a dynamic working environment on a leafy campus close to London, with access to world-class leisure facilities nearby. The role brings a substantial salary and generous relocation package, as well as a variety of academic and professional development opportunities.

The qualifications required and job specification is detailed in the job profile. In addition to completing the online application form, please submit:

• A two-page supporting statement on your future research and research leadership plans
• Your CV
• A list of your publications and externally funded research projects (if not included in your CV)

Interviews will be held remotely in September. Positions are available for immediate appointment and the candidates will be expected to start as early as possible after that and no later than March / April 2022.

Our staff and students come from all over the world and the Department is proud of its friendly and inclusive culture. The University and the Department specifically are committed to building a culturally diverse organisation. Applications are strongly encouraged from female and minority candidates. The Department of Computer Science was awarded a Bronze Athena SWAN award, in recognition of our commitment to equality and diversity.

Applicants are encouraged to check the associated role profile for this post. Informal enquiries are welcomed by Dr Mark Manulis (m.manulis@surrey.ac.uk). Otherwise, we look forward to receiving your online application at: https://jobs.surrey.ac.uk/037121 

--------------------------------------------------------------
Researchers in Speech, Text and Multimodal Machine Translation at DFKI Saarbrücken, Germany
--------------------------------------------------------------

The MT group at ML at DFKI Saarbrücken is looking for

    senior researchers/researchers/junior researchers

in speech, text and multimodal machine translation using deep learning.

3 year contracts. Possibility of extension. Ideal starting dates around June/July 2021.

Key responsibilities:
- Research and development in speech, text and multimodal MT
- Scientific publications
- Co-supervision of BSc/MSc students and research assistants
- Possibility of teaching at Saarland University (UdS)
- Senior: PhD co-supervision
- Senior: Project/grant acquisition and management

Qualifications senior researchers/researchers:
- PhD in NLP/Speech/MT/ML/CS or related
- strong scientific and publication track record in speech/text/multimodal-NLP/MT

Qualifications junior researchers:
- MSc in CS/NLP/Speech/ML/MT or related (possibility to do a PhD at DFKI/UdS)

All:
- Strong background in machine learning and deep learning
- Strong problem solving and programming skills
- Strong communication skills in written and spoken English (German an asset, but not a requirement)

Working environment: the posts are in the “Multilinguality and Language Technology” MLT Lab at DFKI (the German Research Center for Artificial Intelligence https://www.dfki.de/en/web/) in Saarbrücken, Germany. MLT is led by Prof. Josef van Genabith. MLT is a highly international team and does basic and applied research.

Application: a short cover letter indicating which level (senior / researcher / junior) you apply for, a CV, a brief summary of research interests, and contact information for three references. Please submit your application by Friday April 23rd, 2021 as PDF to Prof. Josef van Genabith (josef.van_genabith@dfki.de) indicating your earliest possible start date.  Positions remain open until filled.

Selected MT at MLT group publications 2020/21: Xu et al. Probing Word Translation in the Transformer and Trading Decoder for Encoder Layers. NAACL-HLT 2021. Chowdhury et al. Understanding Translationese in Multi-View Embedding Spaces. COLING 2020. Pal et al. The Transference Architecture for Automatic Post-Editing. COLING 2020. Ruiter et al. Self-Induced Curriculum Learning in Self-Supervised Neural Machine Translation. EMNLP-2020. Zhang et al. Translation Quality Estimation by Jointly Learning to Score and Rank. EMNLP 2020. Xu et al. Dynamically Adjusting Transformer Batch Size by Monitoring Gradient Direction Change. ACL 2020. Xu et al. Learning Source Phrase Representations for Neural Machine Translation. ACL 2020. Xu et al. Lipschitz Constrained Parameter Initialization for Deep Transformers. ACL 2020. Herbig et al. MMPE: A Multi-Modal Interface for Post-Editing Machine Translation. ACL 2020. Herbig et al. MMPE: A Multi-Modal Interface using Handwriting, Touch Reordering and Speech Commands for Post-Editing Machine Translation. ACL 2020. Alabi et al. Massive vs. Curated Embeddings for Low-Resourced Languages: the Case of Yorùbá and Twi. LREC 2020. Costa-jussàet al. Multilingual and Interlingual Semantic Representations for Natural Language Processing: A Brief Introduction. In: Computational Linguistics (CL) Special Issue: Multilingual and Interlingual Semantic Representations for Natural Language Processing. Xu et al. Efficient Context-Aware Neural Machine Translation with Layer-Wise Weighting and Input-Aware Gating. IJCAI 2020

DFKI is one of the leading AI centers worldwide, with several sites in Germany. DFKI Saarbrücken is part of the Saarland University (UdS) Informatics Campus. UdS has exceptionally strong CS and CL schools and, in addition to DFKI, a Max Plank Institute for Informatics, a Max Plank Institute for Software Systems, the Center for Bioinformatics, and the CISPA Helmholz Center for Information Security.

Geographic environment: Saarbrücken  (http://www.saarbruecken.de/en) is the capital of Saarland, one of the Federal States in Germany, located right in the heart of Europe and a cultural center in the border region of Germany, France and Luxembourg. Frankfurt and Paris are less than 2 hours by train. Living cost is moderate in comparison with other cities in Germany and Europe.

Job offer: Post-doc in biomedical image analysis

We are seeking candidates who are interested in developing deep learning algorithms for improving the registration/alignment of our 2D/3D MRI and microscopy images. 

Job description

The selected candidate will join the interdisciplinary Brain/MINDS project which aims at studying the neural networks controlling higher brain functions in the marmoset, to gain new insights into information processing and diseases of the human brain.

As a member of the project, the selected candidate will contribute to the development and implementation of image processing and image analysis techniques with a focus on brain image data, specifically marmosets. We are seeking candidates who enjoy developing deep learning algorithms for 2D/3D image registration / alignment.

The work will be done in a highly interdisciplinary research group consisting of scientists from the neural-scientific and medical research fields. The emphasis is on developing cutting-edge technologies that improve current state-of-the-art and publishing high impact work in top-tier journals in order to build a substantial resume and strong international collaborations.

Experiences in biomedical image analysis is an advantage but not a requirement. This job may be a great opportunity to apply knowledge and expertise from the computer vision and/or image processing field to new problems in the biomedical field.

Location

Wako-City (Kanto district, 2-1 Hirosawa, Wako, Saitama 351-0198). RIKEN is located in very close proximity to the northern part of Tokyo. Map: http://www.riken.jp/en/access/wako-map/.

The RIKEN campus is quite large and offers cafeterias, coffee shops, and a convenient store. From the nearest train station, it is only a 12 min train ride to the Ikebukuro-Station (Tokyo). The Ikebukuro-Station is a hub which connects many famous places in Tokyo, including Shinjuku (9 min train ride), Shibuya (18 min train ride) or Akihabara (19 min train ride). Many people prefer to avoid crowded streets and trains in their daily life and are living in close proximity to RIKEN. However, those who prefer living close to the sightseeing, nightlife and entertainment spots in Tokyo benefit from commuting out of the city in the morning, and returning in the evening (significantly less crowded than the other way around).

Qualifications

The candidate should have or be expecting to receive a Ph.D., by the time of employment, in related fields and have

• relevant research skills and experiences in developing deep learning techniques for the analysis/processing of images, demonstrated by high-quality publications.

• expertise in biological/medical/neural image processing, image registration, computer vision, machine learning, optimization, or similar fields, is an advantage.

• good English communication skills

• proficiency in a programming language (such as C++/Python/JS)

• proficiency in tensorflow, pytorch or a similar DL library

• good communication skills and ability to cooperate

Application & Employment

RIKEN employees enjoy the benefits of a generous vacation and leave package. An overview of benefits can be found here: working-at-riken. For application details, please refer to the official job posting URL: https://cbs.riken.jp/en/careers/20210226_w20294_h.skibbe_r.html.

Closing date: open until the positions are filled.

The Gordon Center for Medical Imaging (GCMI) in the Department of Radiology at Massachusetts General Hospital (MGH) and Harvard Medical School (HMS) in Boston, MA has an immediate opening at the postdoctoral level to work on research projects related to PET and MR image analysis, image restoration and image reconstruction.

The projects are funded by NIH, aiming to utilize machine learning-based image analysis to improve the diagnosis and progression tracking for Alzheimer’s disease (AD) and Neuroendocrine tumor (NET). The projects are based on cooperation with Harvard Aging Brain Study (HABS) and MD Anderson Cancer Center (MDACC).

Qualifications:

Applicants should have earned a Ph.D. in engineering, statistics/mathematics, physics, neuroscience, or a related field. Strong analytical, quantitative and programming skills as well as proficiency in machine learning are essential. Prior experience with PET/MR image analysis is not required. The successful candidate will have joint appointments at MGH and HMS.

Environment:

The Department of Radiology offers extensive core research facilities, including a new digital time-of-flight PET/CT scanner, brain and whole body PET/MRI scanners, and small animal PET/SPECT/CT systems. Studies on these imaging systems are supported by the MGH Gordon PET Core’s fully equipped blood/radiometabolite processing laboratory, on-site cyclotron, dedicated research radiochemistry laboratories, and a world class GMP radiopharmacy. The Gordon Center maintains a large-scale computing facility for image analysis, network training, tomographic reconstruction, Monte Carlo simulation, and other computationally intensive research applications. The successful applicant can interact collaboratively with a large (140+), growing research group in diverse areas of imaging technology and applications.

The position is available as of February 2021 and the start date is flexible. If interested, please send your curriculum vitae, a cover letter describing your background and research interests, and contact information of three references to Dr. Kuang Gong (kgong AT mgh.harvard.edu).

Face recognition has become a key technology in our society, frequently used in multiple applications, while creating an impact in terms of privacy. As face recognition solutions based on artificial intelligence (AI) are becoming popular, it is critical to fully understand and explain how these technologies work in order to make them more effective and accepted by society. In this project, we focus on the analysis of the influencing factors relevant for the final decision of an AI-based face recognition system as an essential step to understand and improve the underlying processes involved. The scientific approach pursued in the project is designed in such a way that it will be applicable to other use cases such as object detection and pattern recognition tasks in a wider set of applications. Thanks to the interdisciplinary nature of the consortium, the outcomes of XAIface will affect many fields and can be summarized as follows: (i) develop clear legal guidelines on the use and design of AI-based face recognition following the privacy-by-design approach; (ii) disentangling demographic information (age, gender, ethnicity) from the overall face representation in order to understand the impact of such traits on face recognition but also to develop demographic-free face recognition; (iii) address fairness and non-discrimination issues by following the idea of de-biasing during the training; (iv) optimize the tradeoff between interpretability and performance; (v) create tools that will allow assessment and measurement of performance and explanation of decisions of AI-based face recognition systems; (vi) analyze image coding impact to better understand how future AI-based coding solutions may be different from a recognition explainability point of view.

This project includes several international teams and will last for 3 years. The working place will be at Instituto de Telecomunicações, Instituto Superior Técnico, Lisboa, Portugal.

Research grant: The research grant is associated to a yearly renewable contract (up to 3 years) that includes an experimental period of 6 months. The research grant consists on a tax-free stipend of 1616€ per month. The candidates must fulfill the following conditions:

• PhD in computer science, electrical and computer engineering or other relevant area, awarded in the past three years.
• Preference will be given to candidates knowledgeable in machine learning, computer vision, multimedia signal processing and face recognition.
• Strong motivation to perform research, to participate in a rich and stimulating international project, and to advance state-of-the-art through the publication of results in peer reviewed international conferences and journals.
• Fluent in English and with good skills in technical writing and presenting.
• Good programming skills (Python, C/C++) are required.

The selected candidate will work in a team lead by Prof. Fernando Pereira and Prof. João Ascenso (see http://www.img.lx.it.pt/Staff.html for details). The candidates will join a team of staff and PhD students where intense research and development activities in the multimedia signal processing and machine learning fields are carried out. 

To apply, please submit your application by sending an email to Prof. Fernando Pereira and Prof. João Ascenso at fp@lx.it.pt and joao.ascenso@lx.it.pt with the following documents:

1. Detailed curriculum vitae with transcripts
2. Motivation letter (research statement) explaining your interest in the position
3. Recommendation letter(s)

Applications shall be received until suitable candidates are found but before 15/2/2021. Selected candidates will be interviewed. For any clarifications, please contact Prof. Fernando Pereira and Prof. João Ascenso.