Tansu Celikel

The School of Psychology (psychology.gatech.edu/) at the GEORGIA INSTITUTE OF TECHNOLOGY (www.gatech.edu/) invites nominations and applications for 5 open-rank tenure-track faculty positionswith an anticipated start date of August 2023 or later. The successful applicant will be expected to demonstrate and develop an exceptional research program. The research area is open, but we are particularly interested in candidates whose scholarship complements existing School strengths in Adult Development and Aging, Cognition and Brain Science, Engineering Psychology, Work and Organizational Psychology, and Quantitative Psychology, and takes advantage of quantitative, mathematical, and/or computational methods. The School of Psychology is well-positioned in the College of Sciences at Georgia Tech, a University that promotes translational research from the laboratory and field to real-world applications in a variety of areas. The School offers multidisciplinary educational programs, graduate training, and research opportunities in the study of mind, brain, and behavior and the associated development of technologies that can improve human experience. Excellent research facilities support the School’s research and interdisciplinary graduate programs across the Institute. Georgia Tech’s commitment to interdisciplinary collaboration has fostered fruitful interactions between psychology faculty and faculty in the sciences, computing, business, engineering, design, and liberal arts. Located in the heart of Atlanta, one of the nation's most academic, entrepreneurial, creative and diverse cities with excellent quality of life, the School actively develops and maintains a rich network of academic and applied behavioral science/industrial partnerships in and beyond Atlanta. Candidates whose research programs foster collaborative interactions with other members of the School and further contribute to bridge-building with other academic and research units at Tech and industries are particularly encouraged to apply. Applications can be submitted online (bit.ly/Join-us-at-GT-Psych) and should include a Cover Letter, Curriculum Vitae (including a list of publications), Research Statement, Teaching Statement, DEI (diversity, equity, and inclusion) statement, and contact information of at least three individuals who have agreed to provide a reference in support of the application if asked. Evaluation of applications will begin October 10th, 2022 and continue until all positions are filled. Questions about this search can be addressed to faculty_search@psych.gatech.edu. Portal questions will be answered by Tikica Platt, the School’s HR director, and questions about positions by the co-chairs of the search committee, Ruth Kanfer and Tansu Celikel.

Tanya Brown

As part of an externally funded project with members of the Cogitate Consortium, we are seeking to hire a Data Scientist or Scientific Software Engineer, ideally one with a background in research data management (RDM), FAIR data, and database administration, who will contribute to establishing the data architecture infrastructure for open and reusable data, generate experimental code, and advance the development of reproducible neuroscience tools and processing pipelines in interdisciplinary research projects. The position will involve creating tools for the efficient organization and exploration of openly shared raw and processed datasets. It is also ideal for networking in the open science community as it includes interaction with the open (neuro)science community; and it will be a unique opportunity for someone keen to contribute to the development of open science and large-scale collaborations, as well as to community efforts and dissemination. 
Your tasks --Preparing and reviewing data for open share with the community; --Developing, testing and implementing scientific software, i.e., reproducible analysis pipelines and data storage for open science building on the BIDS standard; --Reviewing code for reproducible pipelines; --Writing supporting materials and documentation for researcher end users; --Assisting staff with parallelizing scientific software and in the use of cluster and cloud computation; --Providing support and training for data management; --Liaising between the lab and the Institute’s core IT team; --Exchanging and networking within national (NFDI, MPDL, etc.) and international (RDA, EOSC, etc.) initiatives. Our offer We offer an exciting interdisciplinary field of engagement in an international scientific environment. The Institute is located in an attractive location with excellent infrastructure in Frankfurt’s Westend neighborhood. You can expect a modern, well-equipped workplace with flexible working hours (some remote working is possible) and the opportunity to participate in (international) conferences and project meetings. Further development of your personal strengths, e.g., through direct interactions with researchers forming part of the Cogitate Consortium (e.g., Christof Koch, Giulio Tononi, Stanislas Dehaene, Gabriel Kreiman, Ole Jensen, Sylvain Baillet, among many others) is possible. The position will begin earliest on May 1, 2022 and is initially limited to 18 months, with the possibility of an extension pending funding approval. Salary is paid in accordance with the collective agreement for the public sector (TVöD Bund), according to your qualifications and experience. The Max Planck Society strives for gender equality and diversity. We are also committed to increasing the number of individuals with disabilities in our workforce. Therefore, applicants of all backgrounds are welcome. Your application Your application should include: your detailed CV (including details of your educational background and skills); a cover letter that explains why this position interests you and how your skills and abilities are suitable; copies of relevant degrees and/or certificates. Please send these materials all together in a single PDF file, before April 1, 2022, by e-mail to job@ae.mpg.de using the code “TWCF Research Data” in the subject line. Please feel free to contact Tanya Brown (tanya.brown@ae.mpg.de) if you have any questions about the position.

N/A

The Faculty of Engineering and Mathematics at Hochschule Bielefeld – University of Applied Sciences and Arts (HSBI) seeks to fill two positions as Research Associate in the field of Artificial Intelligence (AI) and Machine Learning (ML) within the framework of the research project 'FH-Kooperativ 2-2023: Cognitive Edge Computing for Multi-Sensor Applications with Sparse Data and High Latency Requirements' (Edge4SparseML), funded by the Federal Ministry of Education and Research. The research project's aim is to develop a method toolbox to efficiently run AI/ML procedures on resource-limited hardware for real-time applications. Based on the toolbox, it intends to investigate automated methods to explore the design space of suitable AI/hardware combinations in terms of a hardware/AI co-design. Particular emphasis lies on industrial applications with high latency requirements, considering both the complete chain as a linear process from modelling to inference and the repercussions of the choice of possible hardware configurations on the original modelling.

Harnessing Big Data in Neuroscience: From Mapping Brain Connectivity to Predicting Traumatic Brain Injury

Neuroscience is experiencing unprecedented growth in dataset size both within individual brains and across populations. Large-scale, multimodal datasets are transforming our understanding of brain structure and function, creating opportunities to address previously unexplored questions. However, managing this increasing data volume requires new training and technology approaches. Modern data technologies are reshaping neuroscience by enabling researchers to tackle complex questions within a Ph.D. or postdoctoral timeframe. I will discuss cloud-based platforms such as brainlife.io, that provide scalable, reproducible, and accessible computational infrastructure. Modern data technology can democratize neuroscience, accelerate discovery and foster scientific transparency and collaboration. Concrete examples will illustrate how these technologies can be applied to mapping brain connectivity, studying human learning and development, and developing predictive models for traumatic brain injury (TBI). By integrating cloud computing and scalable data-sharing frameworks, neuroscience can become more impactful, inclusive, and data-driven..

Characterizing the causal role of large-scale network interactions in supporting complex cognition

Neuroimaging has greatly extended our capacity to study the workings of the human brain. Despite the wealth of knowledge this tool has generated however, there are still critical gaps in our understanding. While tremendous progress has been made in mapping areas of the brain that are specialized for particular stimuli, or cognitive processes, we still know very little about how large-scale interactions between different cortical networks facilitate the integration of information and the execution of complex tasks. Yet even the simplest behavioral tasks are complex, requiring integration over multiple cognitive domains. Our knowledge falls short not only in understanding how this integration takes place, but also in what drives the profound variation in behavior that can be observed on almost every task, even within the typically developing (TD) population. The search for the neural underpinnings of individual differences is important not only philosophically, but also in the service of precision medicine. We approach these questions using a three-pronged approach. First, we create a battery of behavioral tasks from which we can calculate objective measures for different aspects of the behaviors of interest, with sufficient variance across the TD population. Second, using these individual differences in behavior, we identify the neural variance which explains the behavioral variance at the network level. Finally, using covert neurofeedback, we perturb the networks hypothesized to correspond to each of these components, thus directly testing their casual contribution. I will discuss our overall approach, as well as a few of the new directions we are currently pursuing.

Foundation models in ophthalmology

Abstract to follow.

Diverse applications of artificial intelligence and mathematical approaches in ophthalmology

Ophthalmology is ideally placed to benefit from recent advances in artificial intelligence. It is a highly image-based specialty and provides unique access to the microvascular circulation and the central nervous system. This talk will demonstrate diverse applications of machine learning and deep learning techniques in ophthalmology, including in age-related macular degeneration (AMD), the leading cause of blindness in industrialized countries, and cataract, the leading cause of blindness worldwide. This will include deep learning approaches to automated diagnosis, quantitative severity classification, and prognostic prediction of disease progression, both from images alone and accompanied by demographic and genetic information. The approaches discussed will include deep feature extraction, label transfer, and multi-modal, multi-task training. Cluster analysis, an unsupervised machine learning approach to data classification, will be demonstrated by its application to geographic atrophy in AMD, including exploration of genotype-phenotype relationships. Finally, mediation analysis will be discussed, with the aim of dissecting complex relationships between AMD disease features, genotype, and progression.

Estimating repetitive spatiotemporal patterns from resting-state brain activity data

Repetitive spatiotemporal patterns in resting-state brain activities have been widely observed in various species and regions, such as rat and cat visual cortices. Since they resemble the preceding brain activities during tasks, they are assumed to reflect past experiences embedded in neuronal circuits. Moreover, spatiotemporal patterns involving whole-brain activities may also reflect a process that integrates information distributed over the entire brain, such as motor and visual information. Therefore, revealing such patterns may elucidate how the information is integrated to generate consciousness. In this talk, I will introduce our proposed method to estimate repetitive spatiotemporal patterns from resting-state brain activity data and show the spatiotemporal patterns estimated from human resting-state magnetoencephalography (MEG) and electroencephalography (EEG) data. Our analyses suggest that the patterns involved whole-brain propagating activities that reflected a process to integrate the information distributed over frequencies and networks. I will also introduce our current attempt to reveal signal flows and their roles in the spatiotemporal patterns using a big dataset. - Takeda et al., Estimating repetitive spatiotemporal patterns from resting-state brain activity data. NeuroImage (2016); 133:251-65. - Takeda et al., Whole-brain propagating patterns in human resting-state brain activities. NeuroImage (2021); 245:118711.

Deep learning applications in ophthalmology

Deep learning techniques have revolutionized the field of image analysis and played a disruptive role in the ability to quickly and efficiently train image analysis models that perform as well as human beings. This talk will cover the beginnings of the application of deep learning in the field of ophthalmology and vision science, and cover a variety of applications of using deep learning as a method for scientific discovery and latent associations.

Artificial Intelligence and Racism – What are the implications for scientific research?

As questions of race and justice have risen to the fore across the sciences, the ALBA Network has invited Dr Shakir Mohamed (Senior Research Scientist at DeepMind, UK) to provide a keynote speech on Artificial Intelligence and racism, and the implications for scientific research, that will be followed by a discussion chaired by Dr Konrad Kording (Department of Neuroscience at University of Pennsylvania, US - neuromatch co-founder)

ReproNim: Towards a culture of more reproducible neuroimaging research

Given the intrinsically large and complex data sets collected in neuroimaging research, coupled with the extensive array of shared data and tools amassed in the research community, ReproNim seeks to lower the barriers for efficient: use of data; description of data and process; use of standards and best practices; sharing; and subsequent reuse of the collective ‘big’ data. Aggregation of data and reuse of analytic methods have become critical in addressing concerns about the replicability and power of many of today’s neuroimaging studies.

Bridging computational neuroscience and genomics in the era of big data