CES 2026 showed that humanoid and embodied AI systems still have a long way to go before delivering real-world value, particularly in homes. At the same time, there is a growing sense that the path to deployment is becoming clearer. A consensus has emerged across platforms: multi-camera perception, often wrist-mounted, paired with VLA models, is sufficient for most tasks. Increasingly, tactile hands and VTLA software are added. There was a clear split between industrial and home-care humanoids.

Researchers at the University of New Orleans want to use artificial intelligence to evaluate and detect potential deficiencies in the United States' floodwater control structures. The proposal includes the creation of an automated program using unmanned aerial system imagery and other sensory data to assess the integrity and stability of the nation's flood control systems. The Joseph Canizaro and James Livingston Gulf States Center for Environmental Informatics (GulfSCEI, pronounced Gulfsea) at the University of New Orleans has secured a one-year contract worth $1.25 million from the U.S. Army Corps of Engineers (USACE) for the research. The research will be performed jointly with USACE's domain experts. As part of its national flood risk management plan, the USACE has planned, designed and constructed over 700 dam and reservoir projects and more than 13,500 miles of federally authorized levees and floodwalls.

Applications are invited for a research associate position in the Department of Mathematics at Imperial College London to work in the area of statistical machine learning with applications in population health. The overall theme of the research is to develop methods in statistical machine learning to study worldwide phenotypes and transitions in multiple health outcomes. The position is funded through an UKRI Medical Research Council grant which involves collaborative research among statisticians and health researchers at Imperial College as well as with a network of scientists from most of the world's countries, which will give the work significant scientific and policy impact and visibility. The post-holder will be based in the vibrant Statistics section of the Department of Mathematics, which is consistently ranked as one of the top in the country for research and has world-class expertise in statistical machine learning, and will collaborate with the Environment and Global Health Research Group (www.globalenvhealth.org) at Imperial School of Public Health. The project will involve the development of Bayesian hierarchical models to identify multimorbidity clusters and investigate the variation in both magnitude and characteristics of these clusters across and within regions of the world.

DECODE is a 30-month research project funded by the NIHR Artificial Intelligence for Multiple Long-Term Conditions (AIM) Programme. This project is led by Loughborough University (PI: Dr Gyuchan Thomas Jun, Reader in Socio-technical System Design) jointly with Leicestershire Partnership NHS Trust (joint PI: Dr Satheesh Gangadharan, Consultant Psychiatrist). Overall, the project team consists of fifteen co-investigators with expertise in the field of intellectual disabilities, neuropsychiatry, epidemiology, health data science, machine learning, data visualisation, human factors, qualitative research and ethics from eight institutions. The co-investigators include Dr Georgina Cosma (AI and data science) and Dr Panos Balatsoukas (UX design) at Loughborough University, Dr Francesco Zaccardi (epidemiology), Dr Michelle O'Reilly (qualitative research) and Prof Kamlesh Khunti (primary care) at the University of Leicester, Ashley Akbari (data science) and Prof Simon Ellwood-Thompson (health informatics) at Swansea University, Dr Vasa Curcin (AI) at King's College London, Prof Rohit Shankar (neuropsychiatry) at the University of Plymouth, Dr Reza Kiani (intellectual disabilities) at Leicestershire Partnership NHS Trust, Dr Neil Sinclair (ethics) at the University of Nottingham, Dr Chris Knifton (nursing) at De Montfort University, and Gillian Huddleston (PPI lead). The DECODE project aims to improve the health and wellbeing of people with intellectual disabilities (also known as learning disabilities) by developing actionable insights to support a model of effective care coordination using machine learning aided analysis of multiple long-term conditions in people with intellectual disabilities.

Job description Artificial intelligence, machine learning and digital twins have the potential to transform cardiology. A cardiac digital twin is the computational replica of the cardiac system of a specific patient. The digital twin provides an unprecedented ability to both depict an integrated and comprehensive diagnostic picture, and to predict the prognosis under a range of therapeutic strategies. We are seeking to appoint a data scientist/engineer to develop and apply the technology that allows the creation of cardiac digital twins at scale. The successful candidate will develop and apply state of the art machine learning and data assimilation methods to automatically analyse longitudinal patient data that will be encoded in a digital twin of the patient's heart.

We have a research associate (postdoc) position to work on spatial audio processing and spatial hearing using methods from machine learning. The aim of the project is to design a method for interactively fitting individualised filters for spatial audio (HRTFs) to users in real-time based on their interactions with a VR/AR environment. We will use meta-learning algorithms to minimise the time required to individualise the filters, using simulated and real interactions with large databases of synthetic and measured filters. The project has potential to become a very widely used tool in academia and industry, as existing methods for recording individualised filters are often expensive, slow, and not widely available for consumers. The role is initially available for up to 18 months, ideally starting on or soon after 1st January 2022 (although there is flexibility).

The role will require the candidate to carry out maintenance and advancement of existing tools along with maintaining data workflows for sharing our data with partner institutions such as PDBe-KB or RNAcentral. In order to advance the tools and methods, the role might require familiarising with the newest advances in machine learning, as well as with the most recent development in the area of structural molecular biology.

We seek to recruit a full-time postdoctoral Research Associate in Microbial genomics and Bioinformatics to work in the laboratory of Dr. Lauren Cowley on an Academy of Medical Sciences springboard scheme funded grant in collaboration with the Gastrointestinal Bacterial reference services at Public Health England (PHE). You will be working on novel machine learning models to predict geographical source attribution from sequencing data of Shiga-toxigenic Escherichia coli and Salmonella. You will be responsible for training, testing and development of prediction models on PHE provided sequencing data to help research the possibilities of using sequencing data to provide automatic prediction of where foodborne disease has originated from; either as a returning traveller, imported food or domestic case. The position is funded at £39,152 and we expect to appoint at this starting salary for a fixed-term period of 15 months. You should hold or be close to completing a PhD in microbiology, genomics, bioinformatics, computer science, applied mathematics or computational biology, with some experience in the development of machine learning prediction models and processing of large microbial sequencing datasets.

Artificial Intelligence (AI) is already re-configuring the world in conspicuous ways. Data drives our global digital ecosystem, and AI technologies reveal patterns in data. Smartphones, smart homes, and smart cities influence how we live and interact, and AI systems are increasingly involved in recruitment decisions, medical diagnoses, and judicial verdicts. Whether this scenario is utopian or dystopian depends on your perspective. The potential risks of AI are enumerated repeatedly.

The UBC Centre for Molecular Medicine and Therapeutics based at the BC Children's Hospital Research Institute is home to a highly collaborative community of scientists connected by a common commitment to use leading edge molecular methods to advance development of therapeutics for human disease. With a strong history in neurogenetics and metabolism research, the CMMT offers one of the premier research environments in Canada for interdisciplinary biomedical research. The Wasserman laboratory creates and applies bioinformatics methods for the study of the human genome. Research projects span the development of machine learning methods and algorithms for the detection of features in genomics data, the application of bioinformatics methods in applied projects such as the identification of genetic sequence variants causing rare disease or the design of gene therapy vectors. The lab members possess expertise spanning disciplines from mathematics to computer science and from human genetics to biochemistry.