Nanoelectronics & Device-Physics Engineer

Astera partnering with the National Science Foundation to support Open Science. Steven Moss Science Policy Lead Jul 22 2026 Today, the National Science Foundation (NSF) announced a partnership with the Astera Institute related to the first round of investment in building a national network of automated laboratory infrastructure, known as the Programmable Cloud Laboratories (PCL) Test Bed Initiative. The NSF will spend $380 million spread across 20 awardees to advance AI and automated instrumentation for scientific experimentation and data collection. The Astera Institute will contribute up to $20 million in matched investment and provide additional support and expertise to awardees to advance their open science practices to make their science more rapidly available, machine-readable, and reproducible. This is the first time Astera is directly partnering with the federal government on a scientific program, but this partnership is not something Astera fell into by accident. Astera is continually looking for ways to promote and empower its approach to experimenting with how science is conducted and funded, including needed changes to scientific publishing that better and more quickly capture the full range of scientific outputs. Improvements on this front are not only enabled by AI but will also help fundamentally advance AI-enabled scientific discovery. Astera's own Open Science Policy espouses the need to get scientific information out quickly using publishing platforms and practices that focus on scientific utility rather than legacy journal systems that are optimized for and constrained by human limitations. To this end, Astera has built an ecosystem of investments and opportunities around open science, including: * The Stacks, an open-access publishing platform Astera is building with the end-goal of experimenting with different ways of making it easier for scientists to generate, structure, and share reproducible data that are geared toward reuse * Funded Programs such as the diffUSE Project, the Deliverome Project, and OpenADMET, which are all working to collect and openly publish AI-ready scientific information * The Hidden Science Competition, a recently announced competition to encourage graduate students to publish useful research beyond the constraints of traditional peer-reviewed scientific journals Astera view its collaboration with the NSF as an important step in adding to the ecosystem of open and rapidly available scientific information that will help to propel innovation forward. Astera is excited about this program and mode of partnership because PCLs create an opportunity to advance a more data-centric model of open science. Rather than treating data as a secondary output released only after an embargo and narrative-centric publication, the PCL Test Bed Program can support workflows in which well-structured, reusable data and metadata are shared rapidly and treated as valuable scientific outputs in their own right. The instrumentation associated with the PCL Test Bed Program emphasizes automated experimentation in controlled environments, enabling researchers to design and test new tools for standardized data-generation processes and capture of high-quality metadata throughout each experimental workflow. This inherently makes experiments more reproducible and data more AI-ready. These capabilities can uniquely enable rapid deposition into repositories and allow researchers and AI agents to more reliably discover, interpret, and reuse scientific outputs at scale while also supporting alternative publishing workflows organized around utility, reuse, and continuous deposition rather than a single final publication event. Through this public-private collaboration, NSF, Astera, and the PCL Test Bed awardees can combine federal leadership, private support, and scientific expertise to make research outputs across fields more AI-ready, interoperable, and reusable - accelerating discovery, increasing the value of publicly funded research, and helping scientific advances translate more rapidly into societal benefit. Steven Moss Science Policy Lead

Identifying systemic bottlenecks to science. Seemay Chou Mar 11 2026 Today, alongside the launch of Radial, Astera is opening an essay competition that I've been ruminating on for some time. Namely, inviting active scientists from any sector to share concrete research challenges that can inform its future work at Astera. Astera is interested in your hypotheses about what broad structural or systemic issues contribute to the bottlenecks you experience in your own science. It's important to me that Astera hear more from active scientists on the ground. Many of its scientific systems and institutions are no longer fit for purpose. How Astera fund work, share results, build teams, and connect science to other disciplines or sectors has long been in need of experimentation. This is no longer a controversial statement. Astera is living through a historical inflection point that demands change. One force is technological, happening at unprecedented scale and speed. AI is making it harder to ignore systemic and infrastructural gaps, while also changing what solutions are possible. This is an incredible forcing function Astera should leverage to update its scientific practices. At the same time, it's become harder to talk constructively about change in light of political differences and more recent budgetary contractions. But it's more important than ever to openly debate long-term reform now. And many disagreements are unlikely to be resolved through debate in the absence of real life testing. Astera is looking to you, scientists. The field of metascience, i.e. the science of science, is often driven today by non-scientists: policy experts, economists, sociologists, psychologists, historians, politicians. Their work can be very useful, but practicing scientists should be more deeply involved in shaping the systems they depend on. Scientists know first-hand what is broken. When scientists themselves have led metascience experiments, the outcomes have often been distinctive and more durable: new institutes structured around questions; focused research organizations built to unlock specific field-level bottlenecks; community infrastructure launched because there was simply no other way to make it happen; critical resources that can't wait for permission. Astera want to help get more scientists in the driver's seat of this conversation and source more hypotheses that can be tested for systemic improvements. Astera want all of it to happen in the open to stimulate more useful public debate about science. And Astera hope that will help the most compelling ideas get real world implementation through support from Astera or others. Examples of what Astera is looking for. Perhaps an easier way to explain what Astera is looking for is to highlight a few historical examples that Astera would have loved to fund early iteration for. Here are a few: * The Protein Data Bank A few crystallographers were frustrated that hard-won structural data was disappearing into individual labs with no way to share it. They bootstrapped a community archive in 1971 with just seven structures and no formal institutional mandate. Astera would have loved to award an essay describing this gap and fund the early bootstrapping required to prototype the foundational data infrastructure for structural biology and drug discovery worldwide. The scientist Paul Ginsparg noticed that his colleagues were emailing preprints to each other and built a centralized server in 1991 to do it better. Astera would have loved to award an essay describing this gap and fund the initial server required to test the utility of what became today's default open publishing infrastructure for physics, math, and computer science. It has since become a general model for the broader open-access movement. * Focused Research Organizations Two scientists, Adam Marblestone and Sam Rodriques, were dead set on trying to generate more connectomics data as a critical public resource for the neuroscience community. This was a defined roadmap that required a start-up-like team, which lacked any dedicated funding mechanism. So they created one by inventing FROs, and it has become an enabling structure for many other projects with similar properties. Astera would have loved to fund early iterations of FRO projects (and Astera did through the first FRO: the longevity-focused Rejuvenome!). * Arcadia Science This one's an experiment I'm directly involved in that's still in a work-in-progress. Arcadia is a for-profit research company co-founded in 2020 by myself and another scientist, Prachee Avasthi. It was motivated by trying to reimagine how Astera could more effectively traverse a wider swath of biology for useful discovery than was possible in its academic labs. Astera asked how Astera could use data to develop organism-agnostic tools, compound broader lessons by sharing more of its work in real time, and open up new funding and sustainability strategies. It would be exciting to fund smaller scale pilots that could inform experiments that lead to new institutes, which can and should be less monolithic than what dominates today. I hope more scientists will join Astera in this dialogue, which is why I've asked that all submissions are public. I know it can sometimes be uncomfortable to put your neck out in this way, but positive change is more likely if Astera normalize open debate. Astera should approach all disagreements according to the scientific principles Astera were trained on. Data, not drama: let's do the experiment. Seemay Chou

Dileep George joins Astera to lead its neuro-inspired AGI effort. Jed McCaleb Co-Founder & CEO, Neuro & AGI Feb 25 2026 Dileep George is joining Astera as Head of AI, leading its AGI research division. Working alongside its Chief Scientist Doris Tsao, he and the team will explore novel, brain-inspired computational architectures to accelerate the development of safe, efficient and aligned AGI. Astera will continue to support this effort with over $1 billion in committed resources over the coming decade. Dileep joins from Google DeepMind, where he worked on frontier AGI research on agents with memory, planning and structure learning. Throughout his career, Dileep has shown that drawing on the computational principles of biological intelligence opens up novel, high-impact pathways for AGI research. At Vicarious, he scaled algorithms for visual processing and reasoning, gaining worldwide attention for breaking text-based CAPTCHAs with human-like data efficiency. He also pioneered AI-powered robotics as a service for industrial applications. At Numenta, he co-developed Hierarchical Temporal Memory, the theoretical framework modeling how the neocortex learns and reasons. Dileep joins Astera alongside Miguel Lázaro-Gredilla, previously a Research Scientist at Google DeepMind. As Research Lead, Miguel will spearhead the development of world models that utilize hierarchical latent variables for long-horizon planning and robust reasoning. Neuro-inspired AGI research is underexplored relative to its potential. The overwhelming majority of AI research today pursues a dominant paradigm: scaling transformer architectures trained on massive datasets. This approach has produced remarkable results and will likely continue to do so, but concentration around any single research direction leaves promising alternatives underexplored. The principles of biological intelligence likely offer novel approaches to AI engineering at scale that aren't captured in existing research paradigms. This could help address two sets of challenges that remain on the path to AGI: 1. Current AI systems lack fundamental capabilities that biological intelligence demonstrates. They can't handle long-range planning that requires maintaining coherent goals across complex action sequences, or learn continuously from experience the way humans do. Massive datasets are still required for tasks where humans need only a handful of examples, and they continue to fail to generalize robustly to situations that differ meaningfully from their training data. 2. The safety and alignment challenges posed by current architectures remain unsolved, even as Astera continue to scale them. Astera don't yet know how to build systems whose goals stay aligned with human values as circumstances change in ways they weren't trained for. Astera can't reliably interpret why models make the decisions they do, which makes it difficult to predict or prevent failures. Commercial investments currently concentrate on scaling transformers, which risks trapping the field in local minima: optimizing a single approach while leaving vast parts of the solution space unexplored. Biological intelligence offers computational principles that current architectures don't capture, opening pathways to systems that are more efficient and more naturally aligned with how humans think and perceive. Bridging neuroscience and AI engineering. Efforts to map biological intelligence - how the brain constructs perception, cognition, and intelligence itself - remain disconnected from the engineering of AI systems. Neuroscience and AI research proceed largely in parallel with limited integration. Providing decade-scale commitment and computational resources, Astera is running two research programs in tight integration: * Decoding the brain's computational architecture: Led by Doris Tsao, Chief Scientist for Astera Neuro, its Neuro division is working to decode the fundamental mechanisms through which the brain constructs intelligence. These capabilities represent some of the hardest unsolved problems in AI, and the brain solves them with remarkable efficiency. * Building AI systems that learn like humans do: Now led by Dileep, its AGI division tackles the research and engineering challenges of building systems that exhibit these capabilities: how intelligent agents adapt continuously to changing environments, correctly attribute rewards to actions in scenarios with sparse feedback, and build hierarchical memory systems that enable efficient retrieval and generalization. Dileep and his team will work closely with Doris, whose work has revealed some of the most detailed accounts of how neural activity produces perception to date. Together, they hope to create an iterative research program where neuroscience discoveries inform engineering approaches, and engineering challenges surface new neuroscience questions. Going forward, Astera hope to see others more tightly link basic neuroscience and applied AI work. This work will be conducted in line with Astera's broader commitment to open science. Astera believe progress on AGI is better served by distributed work across the field than by locking insights away. The team this requires. Astera is now building a team whose capabilities span deep theoretical investigation of biological intelligence, large-scale ML systems engineering, and experimental validation of novel architectures. Astera is actively looking for researchers and engineers with strong machine learning backgrounds and deep curiosity about neuroscience: people who want to investigate what's missing from current approaches and build something better. If this vision excites you - whether you're a researcher, engineer, or someone who wants to work on foundational questions about intelligence - Astera want to hear from you. There's always a need for more ideas and talent in this area. If you have an interesting, underexplored angle you'd like to chase down, Astera has also recently opened a call for applications to the Neuroscience and Artificial Intelligence tracks of Astera's residency program. Its residency is meant to support talented innovators seeding early-stage projects, especially those that might sit outside of what's conventionally pursued. Astera is building a community here that could be a great hub for this type of exploration. Astera hope you will consider applying. Jed McCaleb Co-Founder & CEO, Neuro & AGI