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post-doc: Cartesian Genetic Programming Applied to Histopathological Image Analysis
Where: Toulouse, France France
What: Context and objectives
For about five years, we have been developing in our research team an alternative method to Deep Learning for image analysis. This method uses genetic programming and in particular an approach called Cartesian Genetic Programming (CGP) to assemble already existing image analysis functions. The idea is, rather than having to recreate all the filters "from scratch" as neural networks do, to rely on decades of development in computer science and in particular in image analysis. The goal is to create image analysis pipelines quickly and using very small image datasets. Over the past few years, this method has been successfully applied to video game control with the ATARI benchmark (Wilson 2018) and video images in the context of smart city (Biau 2021)

Recently, a PhD student has developed a library that allows to simply launch the automatic creation of pipelines on images from microscopy on immunological synapses detection problems. We have shown that in a few hours of computation on a dataset composed of 6 to 8 images, our approach allows to obtain accuracies equivalent to deep learning requiring 20 times more images (Cortacero, paper in preparation).

Building on these past successes, we now want to evaluate our approach in more complex situations. In collaboration with the department of histopathology of the Institut Universitaire de Cancérologie de Toulouse (IUCT – CHU Toulouse), we ambition to use our CGP approach in patient tissues. In comparison to our previous work, many challenges must be addressed. Whereas biological images from microscopy are usually very clean and very clear, images from patient can be harder to analyze due to its complexity (many cells of various types) and heterogeneity (differences of staining, machines and technicians) and the size of the pictures (tens of gigabytes).

To overcome this difficulty, the histopathology department have unprecedented data. Thousands of patient tissues have been stained with various makers giving easier access to the point of interests to be identify in images. The first task of the hired candidate will be to evaluate CGP to produce filters to analyze these specific images. We hope being able to easily generate the desired filters which will be helpful to produce a large dataset using standard markers. Classical methods (e.g. deep learning) will then be confronted to CGP on the standard markers to identify areas of interest.

Another objective of this project is to develop new technics with CGP to analyze 3D images. Currently, analyzing biomedical images mainly consists in analyzing 2D images. However, a lot of information is lost by losing the third dimension, often available in patient. In particular, the morphology of the tumor can be very informative to adapt the therapeutic strategy. Few work currently exists on Machine Learning approaches extended to the third dimension. In our opinion, CGP can be instrumental to tackle this objective since it will use past decades of image analysis engineering and research, including 3D image analysis.

Expected competencies
We are looking for candidates graduated in computer science or applied mathematics with skills in evolutionary computation and, if possible, genetic programming. Candidates must have undeniable coding and scientific paper writing experiences. Skills in image analysis will be appreciated, in particular in the field of medical images. Candidates must be interested in collaborating with medical doctors as they will be co-supervised by expert in genetic programming and doctor in histopathology (see section “Supervision” above).

Supervision
The candidate will be supervised by:
- Sylvain Cussat-Blanc, Hervé Luga & Jean-Marc Alliot, IRIT CNRS UMR5505, experts in evolutionary computation and applications to biomedical images
- Camille Franchet & Pierre Brousset, IUCT CHU Toulouse, experts in histopathology
The hired candidate will be work at CRCT in the IRIT-CRCT project team. Computer science and artificial intelligence are taking an increasing place in the world of medical research, and in particular in the world of cancer research. The joint IRIT/CRCT team, co-located on the Toulouse Oncopole site, aims to have computer scientists, cancer researchers and physicians work together on the same site.

Contract
12-months contract with a possible extension to 12 additional months. Salary will be discussed depending on the experience of the candidates. The postdoc contract can start as soon as possible and will be situated at CRCT, 2 avenue Hubert Curien, 31000 Toulouse, France.

References
Biau, J.; Wilson, D.; Cussat-Blanc, S. and Luga, H. (2021). Improving Image Filters with Cartesian Genetic Programming. In Proceedings of the 13th International Joint Conference on Computational Intelligence - ECTA,ISBN 978-989-758-534-0; ISSN 2184-2825, pages 17-27. DOI: 10.5220/0010640000003063

Wilson, D.G., Cussat-Blanc, S., Luga, H. and Miller, J.F., 2018, July. Evolving simple programs for playing Atari games. In Proceedings of the Genetic and Evolutionary Computation Conference (pp. 229-236).

Who: sylvain.cussat-blanc@irit.fr
When: Until 2022-05-30 18:00
Presented at next GECCO?: yes

post-doc: Postdoctoral Research Fellowship in Interpretable and Fair Machine Learning for Clinical Decision Support
Where: Boston, MA, USA, United States United States
What: The Cava Lab at Boston Children’s Hospital / Harvard Medical School is seeking a post-doctoral research fellow to advance the interpretability and fairness of machine learning (ML) models deployed in critical healthcare settings. The fellow will join a multi-disciplinary team of computer scientists, informaticists, clinicians, engineers and bioethicists to develop and assess clinical prediction algorithms and advance our understanding of the behavior of machine learning models deployed in health settings. The fellow will help us think critically about how machine learning methods affect clinical practice and outcomes; in particular, 1) the conditions under which ML provides or fails to provide insight into disease pathologies, and 2) the conditions under which ML exacerbates or mitigates treatment and outcome disparities between patient subgroups.

Prediction models are an increasingly important technology in the digital health landscape, and can produce large-scale changes in health care via their interactions with patients, clinicians, and hospital operations. This postdoctoral fellowship provides an opportunity to study these issues more deeply in order to improve our ability to diagnose and intervene in a more trustworthy and equitable way.

The fellowship includes an academic appointment at Harvard Medical School, as well as a hospital appointment at Boston Children’s Hospital. This position provides an excellent opportunity for the Research Fellow to work within a multidisciplinary research team to explore advanced areas in health information technology. CHIP is home to 20 faculty working at the forefront of research areas extending beyond clinical prediction algorithms to domains like clinical NLP, digital epidemiology, clinical genomics, and app ecosystems for health records. CHIP and the CAVA Lab value diversity and believe that it is essential to our research goals. We therefore strongly encourage candidates from underrepresented groups to apply.
Admissions

The position is available immediately and is renewable annually.
Qualifications

PhD degree in computer science, information science, biomedical informatics, data mining, engineering, applied mathematics, or a closely related field.
A track record of high-quality research that demonstrates the ability to independently identify important research topics and carry out experiments.
Candidates with strong experience in machine learning, preferably both in the assessment of ML algorithms in data science applications and in the development of novel methods.
Experience and familiarity with the machine learning literature on interpretability and fairness
Experience working with large, heterogeneous data collections, especially electronic health records, multi-omics data, or other health data
Programming experience in Python, R, and/or C++
Experience with collaborative software development (revision control, continuous integration, etc) strongly preferred
Experience with open science practices (preprints, reproducible workflows, etc) strongly preferred
Strong written and oral communication skills
Ability to work both independently and as a team player

How to apply

Interested candidates should email a CV, three letters of reference, and a sample publication to Dr. William La Cava, PI: william.lacava at childrens.harvard.edu.

Who: William La Cava, william.lacava@childrens.harvard.edu.
When: Until 2022-08-17 00:00
Presented at next GECCO?: yes

PhD: 4 PhD Positions in Computer Science at NUI Galway, Ireland
Where: Galway, Ireland Ireland
What: I have openings for 4 PhD students within my research group in the School of Computer Science at NUI Galway. The start date for these positions is the 1st of October 2022. The successful candidates will work on an interdisciplinary project developing Artificial Intelligence (AI) algorithms and applying these AI algorithms to integrate renewable energy within the agriculture sector.

See the following link for full details and instructions on how to apply:

https://karlmasonsite.files.wordpress.com/2022/04/phd_add_nuig_karlmason.pdf

Deadline for applicants: No fixed deadline. Applications will be accepted until all positions are filled.

Who: Karl Mason
When: Until 2022-07-31 12:00
Presented at next GECCO?:

post-doc: Postdoctoral Researcher in Computer Science at NUI Galway, Ireland
Where: Galway, Ireland Ireland
What: I have an opening for a Postdoctoral Researcher within my research group in the School of Computer Science at NUI Galway. The start date for this position is the 1st of October 2022. The successful candidate will work on an interdisciplinary project developing Artificial Intelligence (AI) algorithms and applying these AI algorithms to integrate renewable energy within the agriculture sector.

If your PhD is near completion and you are interested in conducting postdoctoral research, see the following link for full details and instructions on how to apply:

https://karlmasonsite.files.wordpress.com/2022/04/nuig-res-089-22-job-advert.pdf

Deadline for applicants: 31st of July 2022

Who: Karl Mason
When: Until 2022-07-31 12:00
Presented at next GECCO?:

PhD: Research Analyst (Junior and Senior)
Where: Edinburgh, United Kingdom United Kingdom
What: This position is ideal for a recent PhD Graduate or PostDoc with experience in Evolutionary Computation and good programming skills. At Level E we research and develop systems that learn in dynamic environments from large data sets.

This is an excellent opportunity to learn and develop your skills in Level E’s pioneering work in autonomous investing; a stimulating and challenging international industry with unlimited potential. Also if you are looking for a rewarding career working in a vibrant environment and a diverse culture that thrives on shared success and innovative ways of thinking, this could be the role for you.

You will be joining an innovative company where you can have a real impact working with a diverse and smart team. Having the track record and being positioned in a high-impact and high-growth area helps us provide a working environment where you can develop your skills while working closely with everyone else in the firm.

Level E Research is committed to Equal Employment Opportunity through attracting and retaining a diverse team and building an inclusive environment for all.

Who: Dr. Sonia Schulenburg <admin@leveleresearch.com>
When: Until 2022-07-30 19:00
Presented at next GECCO?: no

research engineer in industry: Autonomous Systems Research Scientist
Where: Reston, VA, United States United States
What: Metron is an employee-owned company dedicated to delivering innovative solutions for the most challenging national security problems. For over 35 years, our principled approach to problem-solving has yielded creative solutions at the intersection of advanced mathematics, computer science, physics, and engineering. Our people are leaders in their technical fields and are passionate about solving challenging problems. We look for individuals who share this same passion and can apply their experience in real-world settings.

RESILIENT MISSION AUTONOMY
Our unmanned systems team is adding a talented research scientist with a background in autonomous systems, robotics, and/or machine learning. Our team is dedicated to creating resilient mission autonomy via the development of technologies that increase the reliability and longevity of unmanned vehicles in some of the world’s most demanding environments, on some of the most complex of missions. The successful candidate will be passionate about designing, developing, and applying cutting-edge autonomy technologies for prototype maritime systems.

In this position, you will develop and contribute ideas for autonomous vehicle technologies in navigation, command and control, and decision making. You will be part of a team that designs and implements some of the most capable and advanced autonomous software systems on or off the planet. You will integrate into, and lead, dynamic and highly motivated project teams, and use new ideas from across multiple fields to push the boundaries of the possible for intelligent systems.

Required Qualifications
Master’s degree in computer science, robotics, mathematics, physics, or applicable engineering field
At least 2 years of relevant maritime autonomous systems experience
Object-oriented programming proficiency
Ability to work both collaboratively as part of a team and independently with minimal supervision
US CITIZENSHIP REQUIRED

Desired Qualifications
Advanced degree in robotics, computer science, physics, or applicable engineering field
Experience in autonomy and autonomous decision making, underwater navigation, automated planning, and/or machine learning
Programming experience in modern C++
Experience in R&D environments and with rapid software prototyping
Ability to improvise and think “outside the box” to develop technical solutions
Excellent communication skills, both written and oral
A track record of publications and/or successful proposal writing
Experience with Federal S&T and R&D sponsors (DOE, NOAA, DARPA, ONR, NASA) and contract vehicles (BAAs, SBIRs)
Experience in the offshore maritime industry
Active DOD clearance

Position Location: Reston, VA

Workplace Safety
Metron follows the CDC and OSHA guidance for workplace safety. We also listen to our employees to create a safe and workable office environment for everyone. Metron continues to support flexible/hybrid telecommuting options when project work allows for working from home. If you have any questions about the safety precautions we are taking, we encourage you to ask during the interview process.

Perks and Benefits
Medical, Dental and Vision Insurance
Accompanying FSA and HSA options
Additional Voluntary Benefits
Paid Time Off
9 Observed Holidays and 2 Floating Holidays
Paid Parental Leave
Tuition Reimbursement
Professional Development Reimbursement
Annual Salary Reviews
Profit Sharing
401(k) Traditional and Roth Options
Gym and Fitness Reimbursement
Employee Assistance Program
Employee Referral Program

All qualified applicants will receive consideration for employment and will not be discriminated against on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, or protected veteran status. Metron takes affirmative action in support of its policy to employ and advance in employment individuals who are minorities, women, protected veterans, and individuals with disabilities.

VEVRAA Federal Contractor

Who: Shannon Lane, Corporate Recruiter, lane@metsci.com
When: Until 2022-08-26 00:00
Presented at next GECCO?: yes

research position: 2 PhD and 1 Postdoc positions on Distributed Intelligence and Learning
Where: Trento, Italy Italy
What: For an upcoming European project (starting in October 2022), I am looking for 2 PhD candidates with a Master degree in Computer Science, Information Engineering, or equivalent topics, and 1 postdoc with previous experience in machine learning/evolutionary computation, and, possibly, distributed systems, in particular sensor networks. All the positions are fully funded for 3 years. The PhD candidates must start on November 1, 2022, while the starting date for the postdoc can be negotiated. Each PhD scholarship is approximately 1200 EUR/month (after tax), while the salary for the postdoc is approximately 2500 EUR/month (before tax).

The project will focus on the design and development of lightweight machine learning models for sensor systems to be used in smart building applications.

On the one hand, we will investigate lightweight black-box (e.g., neural networks) and white-box (e.g., decision trees) models, and combinations thereof. In this regard, we will use Neural Architecture Search and evolutionary algorithms to derive optimally designed models that take into account possible computational and energy constraints on the nodes of the distributed system. We will also study the explainability of those models.

On the other hand, we will study different distributed learning approaches, based on consensus and other forms of aggregation of the outputs of the node-local models, in order to achieve a global goal at network level. Federated and split learning approaches, also including probabilistic learning and evolutionary optimization, will be considered to ensure a flexible architecture capable of guaranteeing a proper separation of concerns and data protection/privacy.

For all the positions, previous experience in sensor networks, embedded systems, and probabilistic communication is considered a plus.

Who: Giovanni Iacca, giovanni.iacca@unitn.it
When: Until 2022-08-30 18:00
Presented at next GECCO?: yes

PhD: Mitigation of Reinforcement Learning Algorithms in Changing Environments
Where: Manchester, United Kingdom United Kingdom
What: The development of (deep) Reinforcement Learning (RL) algorithms to train agents within game environments is well known. Agent training is typically conducted against a known, simplified, or constrained environment. However, the deployed environment is typically more complex, and subject to some change and uncertainly not represented in the training environment. RL algorithms typically characterise performance against probabilistic arenas, rather than being able to cope with an environment that is subject to change over time. The performance of the resulting RL agent can then be expected to become compromised over time, but not necessarily be catastrophic. In this PhD project, we are concerned with (i) understanding this performance degradation and (ii) the development of mitigating strategies. More specifically, the project will focus at creating a train-and-test framework comprising a simulation engine for dynamic environment and a configurable RL approach. In addition to considering changes in the environment, the simulator and RL agent will need to account for real-world challenges, such as multiple conflicting objectives, robustness, and safety issues.

Who: Richard Allmendinger, richard.allmendinger@manchester.ac.uk
When: Until 2022-06-09 19:00
Presented at next GECCO?: yes

PhD: Mitigation of Reinforcement Learning Algorithms in Changing Environments
Where: Manchester, United Kingdom United Kingdom
What: More information about the project can be found here https://www.jobs.ac.uk/job/CPP718/epsrc-bae-systems-industrial-case-phd-studentship-mitigation-of-reinforcement-learning-algorithms-in-changing-environments

Theme of the PhD project:
The development of (deep) Reinforcement Learning (RL) algorithms to train agents within game environments is well known. Agent training is typically conducted against a known, simplified, or constrained environment. However, the deployed environment is typically more complex, and subject to some change and uncertainly not represented in the training environment. RL algorithms typically characterise performance against probabilistic arenas, rather than being able to cope with an environment that is subject to change over time. The performance of the resulting RL agent can then be expected to become compromised over time, but not necessarily be catastrophic. In this PhD project, we are concerned with (i) understanding this performance degradation and (ii) the development of mitigating strategies. More specifically, the project will focus at creating a train-and-test framework comprising a simulation engine for dynamic environment and a configurable RL approach. In addition to considering changes in the environment, the simulator and RL agent will need to account for real-world challenges, such as multiple conflicting objectives, robustness, and safety issues.

The team at BAE Systems is focused on cutting-edge research in advanced simulation, optimization, and machine learning, and are thus invested in how RL can be extended to support decision making in dynamic environments. The project will therefore contribute directly to BAE Systems’ ongoing research. From a scientific perspective, this project will lead to cross-disciplinary research and output that is of high quality and significance.

Due to the nature of this topic, candidates may be subject to a security check.

Supervision:
The successful candidate will be supervised by Dr. Richard Allmendinger, Dr. Theodore Papamarkou and Dr. Wei Pan from The University of Manchester.

Nature of the studentship:
The 4-year studentship will commence in September 2022, covering full tuition fees and a stipend equivalent to UKRI rates (£16,062 tax free for 2022/23; subject to change for future years), plus an industrial top-up stipend from BAE Systems, subject to contract. It also provides travel support for fieldwork, conferences and annual visits to BAE Systems.

Entry Requirements:
Applications are sought from talented and motivated Home and International candidates with an academic background in at least one of these fields: (Deep/multi-agent) reinforcement learning, deep learning, Gaussian processes, Bayesian optimization, transfer/online/meta-learning/safe/multitask learning, dynamic control.

Applicants must hold:
- 1st or 2:1 Honours degree (or equivalent), and
- Masters degree with Distinction
- English Language requirements (where required) are IELTS 7.0, TOEFL 623 (100 ibt), PTE 76.

How to apply: Candidates should submit a PhD application for the PhD Business & Management, and indicate that they wish to be considered for the EPSRC/BAE Systems INDUSTRIAL CASE PhD Studentship.

The application must contain:

- A research proposal (500-1000 words) related to the topic, and
- A written statement clearly indicating how your research competencies and interests to date are aligned with the specific nature of the PhD projects.

If you do not submit the required supporting documents outlined above by the deadline, your application will not be considered.

Who: Richard Allmendinger, richard.allmendinger@manchester.ac.uk
When: Until 2022-06-09 19:00
Presented at next GECCO?: yes

PhD: Master/PhD Scholarship at the University of Tsukuba
Where: University of Tsukuba, Japan Japan
What: This is a recommendation based scholarship offered by the Human Centered Artificial Intelligence graduate program at the University of Tsukuba, Japan. The scholarship fully covers tuition as well as a monthly stipend. The successful candidate will enter a Master (2 years) or PhD (3 years) graduate program, beginning in April, 2023, with the research topic involving either Design Optimization using Evolutionary Computation, or Multi-Agent Simulations of Evacuation and Disease Transmission.

More details at:
https://www.cs.tsukuba.ac.jp/hcaip/

Who: Claus Aranha, caranha@cs.tsukuba.ac.jp
When: Until 2022-07-30 22:59
Presented at next GECCO?: no



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