The Chair of Lifespan Developmental Neuroscience investigates neurocognitive mechanisms underlying perceptual, cognitive, and motivational development across the lifespan. The main themes of our research are neurofunctional mechanisms underlying lifespan development of episodic and spatial memory, cognitive control, reward processing, decision making, perception and action. We also pursue applied research to study effects of behavioral intervention, non-invasive brain stimulation, or digital technologies in enhancing functional plasticity for individuals of difference ages. We utilize a broad range of neurocognitive (e.g., EEG, fNIRs, fMRI, tDCS) and computational methods. The here announced position is embedded in a newly established research group funded by the DFG (FOR5429), with a focus on modulating brain networks for memory and learning by using focalized transcranial electrical stimulation (tES). The subproject with which this position is associated will study effects of focalized tES on value-based sequential learning at the behavioral and brain levels in adults. The data collection for this subproject will mainly be carried out at the Berlin site (Center for Cognitive Neuroscience, FU Berlin).

We are announcing one or more 2-year postdoc positions in identification and analysis of lexical semantic change using computational models applied to diachronic texts. Our languages change over time. As a consequence, words may look the same, but have different meanings at different points in time, a phenomenon called lexical semantic change (LSC). To facilitate interpretation, search, and analysis of old texts, we build computational methods for automatic detection and characterization of LSC from large amounts of text. Our outputs will be used by the lexicographic R&D unit that compiles the Swedish Academy dictionaries, as well as by researchers from the humanities and social sciences that include textual analysis as a central methodological component. The Change is Key! program and the Towards Computational Lexical Semantic Change Detection research project offer a vibrant research environment for this exciting and rapidly growing cutting-edge research field in NLP. There is a unique opportunity to contribute to the field of LSC, but also to humanities and social sciences through our active collaboration with international researchers in historical linguistics, analytical sociology, gender studies, conceptual history, and literary studies.

The College of Information Sciences and Technology at Penn State is seeking a postdoctoral scholar to join an interdisciplinary team focusing on Human-AI collaboration to train mental health workers. The project is supported by an NSF grant. The position will involve developing computational methods to assess clinical sessions and provide actionable feedback to support effective training.

The College of Information Sciences and Technology at Penn State is seeking a postdoctoral scholar to join an interdisciplinary team focusing on Human-AI collaboration to train mental health workers. The project is supported by an NSF grant. The position will involve developing computational methods to assess clinical sessions and provide actionable feedback to support effective training.

PhD positions are available in Norbert Kopco's Perception and Cognition Lab in the Institute of Computer Science at Safarik University in Kosice, Slovakia, for a Marie Curie EU-funded project on Spatial Audio Virtualization and Gamification for Hearing Assessment and Enhancement. This is an international collaborative project, combining psychophysical, neuroimaging and computational methods to study and enhance the hearing abilities. As part of this project, the student would have the opportunity to work on development of modules for an auditory training game and spend up to 12 months in the US with one or more of the grant collaborators (Oregon Health Science University, Northeastern University, Mass General Hospital/Harvard Medical School, Boston University).

The College of Information Sciences and Technology at Penn State is seeking a postdoctoral scholar to join an interdisciplinary team focusing on Human-AI collaboration to train mental health workers. The project is supported by an NSF grant. The position will involve developing computational methods to assess clinical sessions and provide actionable feedback to support effective training. An ideal candidate will have strong research skills and experience in relevant areas (e.g., foundation models, deep learning, natural language processing, reinforcement learning). It will be a full-time appointment for 24 months, with a possibility of renewal dependent upon performance.

We are looking for a new team member with the following profile: You have an open-minded and collaborative attitude towards doing groundbreaking digital humanities research; You have a firm grasp on computational methods and are willing to achieve proficiency in historical research methods; You have a Ph.D. degree in semantic web, digital humanities, language technology, or a related field by the starting date of the project; Your English is excellent and you have or are willing to obtain a working proficiency in Dutch. Why you should consider working with us: Location, location, location: Our offices are located in a historical building in downtown Amsterdam. Researchers work together in projects and a shared office space and can choose to spend part of their time at home. The team: We value a social, open and inquisitive, safe work environment where your input counts, not your job title, this means open conversations about pros and cons of a particular idea and approach based on content, not status. We also brew a mean cup of coffee and the office has a cooking club. Good to know: The Royal Netherlands Academy of Arts and Sciences is not an educational institution, this means that you can focus on building your research profile without a teaching load. We do work together with various Dutch universities so some teaching is an option. About the procedure: Apply by 10 January 2025 via https://vacatures.knaw.nl/job-invite/2438/. Please do not provide more information than what is requested (motivation letter, CV, 1-page research-statement and one illustrative publication). If the committee requires more information, they will ask. The hiring committee will review the applications and invite candidates for an online interview with an optional second interview. The first round of interviews will take place online on 22 January. Starting date & duration: the starting date is negotiable but the project team prefers to fill the vacancy sooner rather than later. The position is for three years.

The Allen Institute is searching for a visionary leader to direct its new Center for Data-Driven Discovery, Studio D3. Studio D3 develops and applies cutting-edge theoretical models, analytical frameworks, and scalable computational methods to extract principles that govern biology from multimodal biological data. The Allen Institute has collected and openly shared some of the largest datasets in life sciences. By integrating computation, data science, and quantitative modeling into the research ecosystem, Studio D3 helps drive discovery across diverse biological disciplines.

AI embedded in real systems, such as in satellites, robots and other autonomous devices, must make fast, safe decisions even when the environment changes, or under limitations on the available power; to do so, such systems must be adaptive in real time. To date, edge computing has no real adaptivity – rather the AI must be trained in advance, typically on a large dataset with much computational power needed; once fielded, the AI is frozen: It is unable to use its experience to operate if environment proves outside its training or to improve its expertise; and worse, since datasets cannot cover all possible real-world situations, systems with such frozen intelligent control are likely to fail. Lifelong Learning is the cutting edge of artificial intelligence - encompassing computational methods that allow systems to learn in runtime and incorporate learning for application in new, unanticipated situations. Until recently, this sort of computation has been found exclusively in nature; thus, Lifelong Learning looks to nature, and in particular neuroscience, for its underlying principles and mechanisms and then translates them to this new technology. Our presentation will introduce a number of state-of-the-art approaches to achieve AI adaptive learning, including from the DARPA’s L2M program and subsequent developments. Many environments are affected by temporal changes, such as the time of day, week, season, etc. A way to create adaptive systems which are both small and robust is by making them aware of time and able to comprehend temporal patterns in the environment. We will describe our current research in temporal AI, while also considering power constraints.

If we can make computers play chess and even Jeopardy and Go, then why can't we make them see like us? How does our brain solve the problem of seeing? I will describe some of our recent insights into understanding object recognition in the brain using behavioral, neuronal and computational methods.

The combination of two-photon microscopy recordings and powerful calcium-dependent fluorescent sensors enables simultaneous recording of unprecedentedly large populations of neurons. While these sensors have matured over several generations of development, computational methods to process their fluorescence are often inefficient and the results hard to interpret. Here we introduce Suite2p: a fast, accurate, parameter-free and complete pipeline that registers raw movies, detects active and/or inactive cells (using Cellpose), extracts their calcium traces and infers their spike times. Suite2p runs faster than real time on standard workstations and outperforms state-of-the-art methods on newly developed ground-truth benchmarks for motion correction and cell detection.

Neurons and neural circuits can produce stereotyped and reliable output activity on the basis of highly variable cellular, synaptic, and circuit properties. This is crucial for proper nervous system function throughout an animal’s life in the face of growth, perturbations, and molecular turnover. But how can reliable output arise from neurons and synapses whose parameter vary between individuals in a population, and within an individual over time? I will review how a combination of experimental and computational methods can be used to examine how neuron and network function depends on the underlying parameters, such as neuronal membrane conductances and synaptic strengths. Within the high-dimensional parameter space of a neural system, the subset of parameter combinations that produce biologically functional neuron or circuit activity is captured by the notion of a ‘solution space’. I will describe solution space structures determined from electrophysiology data, ion channel expression levels across populations of neurons and animals, and computational parameter space explorations. A key finding centers on experimental and computational evidence for parameter correlations that give structure to solution spaces. Computational modeling suggests that such parameter correlations can be beneficial for constraining neuron and circuit properties to functional regimes, while experimental results indicate that neural circuits may have evolved to implement some of these beneficial parameter correlations at the cellular level. Finally, I will review modeling work and experiments that seek to illuminate how neural systems can homeostatically navigate their parameter spaces to stably remain within their solution space and reliably produce functional output, or to return to their solution space after perturbations that temporarily disrupt proper neuron or network function.

Microorganisms can swim in a variety of environments, interacting with chemicals and other proteins in the fluid. In this talk, we will highlight recent computational methods and results for swimming efficiency and hydrodynamic interactions of swimmers in different fluid environments. Sperm are modeled via a centerline representation where forces are solved for using elastic rod theory. The method of regularized Stokeslets is used to solve the fluid-structure interaction where emergent swimming speeds can be compared to asymptotic analysis. In the case of fluids with extra proteins or cells that may act as friction, swimming speeds may be enhanced, and attraction may not occur. We will also highlight how parameter estimation techniques can be utilized to infer fluid and/or swimmer properties.