Early Career Software Engineer

Oceans contain billions of tonnes of uranium. The attraction of seawater uranium is the sheer size of the potential resource. Known uranium resources on land are estimated at around 7.9 million tonnes, while the world's oceans are estimated to contain approximately 4.5 billion tonnes of uranium. Much of it entered the oceans over geological timescales as uranium was weathered from rocks and carried into the sea. For countries expanding nuclear power, that could represent a potentially enormous additional source of nuclear fuel. China is particularly dependent on uranium imports. According to the World Nuclear Association figures cited by South China Morning Post (SCMP), Chinese mines produced about 1,600 tonnes of uranium in 2024, compared with roughly 13,000 tonnes required by its nuclear reactors. The US has spent decades investigating whether seawater could help close this gap. Researchers at Oak Ridge National Laboratory and Pacific Northwest National Laboratory developed and tested uranium-absorbing fibers in natural seawater, including experiments at Washington's Sequim Bay. The US program ultimately found that the economics remained unfavorable compared with conventional land-based mining. PhosCage has so far only been demonstrated under controlled laboratory conditions. The Qingdao researchers collected seawater off the coast of Qingdao and processed 25 liters through a laboratory flow system rather than deploying the material directly in the ocean. Large-scale ocean deployment would introduce challenges involving currents, waves, fouling by marine organisms, material durability, recovery of the adsorbent, and processing costs. The researchers also did not provide a cost estimate for the uranium recovered in their experiments. They say their next steps include scaling up the material and reducing the overall cost of extracting uranium from seawater. Get the latest in engineering, tech, space & science - delivered daily to your inbox. You may unsubscribe at any time. Kaif Shaikh is a journalist and writer passionate about turning complex information into clear, impactful stories. His writing covers technology, sustainability, geopolitics, and occasionally fiction. A graduate in Journalism and Mass Communication, his work has appeared in the Times of India and beyond. After a near-fatal experience, Kaif began seeing both stories and silences differently. Outside work, he juggles far too many projects and passions, but always makes time to read, reflect, and hold onto the thread of wonder.

FMS 2026: Liqid launches the industry's most advanced CXL memory pooling platform for AI and scientific discovery. With Micron Technology in powering the Pacific Northwest National Laboratory's large-scale memory-centric computing platform. Liqid, a player in software-defined memory and GPU pooling infrastructure, announced its role as a strategic technology partner in Abaco, the memory-centric computing system designed by Micron, a subcontractor to the US Department of Energy's (DOE) Pacific Northwest National Laboratory (PNNL).Liqid is providing the scale-up memory pool that allows PNNL to expose hundreds of terabytes of coherent active memory to data-intensive AI for Science workloads. Solving the Memory Wall for Scientific AI For decades, memory architectures have limited applications such as computational chemistry, molecular biology, and AI-driven simulation by restricting access to the large memory capacity they require. The Abaco system pioneers CXL-enabled memory pooling and sharing, shattering the capacity limits of individual servers to support massive-scale AI workloads. Liqid delivers the industry's first and only fully disaggregated, software-defined memory pooling solution. In a Liqid rack-scale configuration, up to 160TB of DRAM can be dedicated to a single host or dynamically allocated and shared across as many as 16 server nodes. Real-time allocation of terabytes of memory enables zero stranded capacity and massive performance scaling when needed. What Liqid Contributes to PNNL * Liqid EX-5410C Memory Platform: CXL 2.0 memory expansion system supporting up to 40TB of DRAM per chassis and scaling to a unified memory pool of more than 160TB for memory-intensive AI, HPC, and database workloads * Liqid Matrix Software: The orchestration and management layer that turns pooled memory into a programmable resource. Liqid Matrix delivers the industry's only unified interface for real-time deployment, management, and orchestration of composable GPU, memory, and storage - and integrates with Kubernetes, Slurm, Ansible, and other tooling already used in scientific computing environments * Liqid CXL 2.0 Fabric: A dedicated CXL 2.0 switch and HBAs deliver ultra-low-latency, high-bandwidth connectivity between hosts and the memory pool Memory Sharing and Benchmark Results Liqid and Micron have also collaborated to enable the industry's first architecture that supports true memory-sharing capabilities. Micron's famfs Linux kernel enhancement enables advanced memory sharing across systems with software cache coherence, laying the foundation for future performance gains through larger shared memory pools, reduced data movement, and more efficient access to massive datasets. With famfs, disaggregated memory pools from the Liqid system become exposed to the host operating system as a file system, requiring no application layer changes to harness the latency and bandwidth advantages of pooled memory. Using the famfs Linux kernel in the Abaco system with Micron high-capacity RDIMMs, Micron lab benchmarks using representative workloads demonstrated performance improvements. Results showed up to 30x faster processing time for graph analytics workloads and up to 7x more tokens per second for certain KV cache workloads, validating the power of CXL memory pooling for AI and scientific computing, with additional optimization and performance gains expected as the platform enables new features. Executive Perspectives "PNNL is exactly the kind of environment that proves what composable CXL memory can do at the frontier of science. PNNL and Micron have built something singular: a memory-centric system designed for the workloads that could define the next decade of AI for Science. Liqid is honored to extend that platform with the scale, orchestration, and composability that turn pooled memory into a programmable resource. This is what tokens per dollar and tokens per watt look like in a national laboratory environment," said Sumit Puri, founder and CTO, Liqid. "PNNL's work demonstrates how CXL-based memory pooling can extend system memory resources beyond the limits of individual servers. By combining Micron high-capacity DRAM with a disaggregated memory architecture, the Abaco system provides researchers with access to larger pools of memory for scientific computing workloads. This project highlights an innovative approach to addressing memory challenges as computing requirements continue to grow." added Vijay Nain, senior director, emerging memory and ecosystem development, Micron Technology. "The Abaco system testbed design increases memory capacity by orders of magnitude, which is critical for scientific workloads. We look forward to quantifying that performance improvement in HPC, AI inference, and post-training applications," concluded James A. Ang, Ph.D., chief scientist, computing, IDSD, Pacific Northwest National Laboratory. Why It Matters for AI for Science PNNL will leverage Abaco to advance AI for Science, with AI-integrated computational chemistry as the first user-facing workload. By extending the lab's pooled memory with Liqid's CXL memory fabric, the system can keep larger structures, larger parameter spaces, and heavier intermediate data resident in active memory, directly attacking the memory bottleneck that has historically constrained scientific AI. PNNL's testbed is available to DOE-funded researchers from national laboratories and academia through the Advanced Memory to Support Artificial Intelligence for Science (AMAIS) initiative, funded by the DOE Advanced Scientific Computing Research program within the DOE Office of Science. Read also: Switchtec PCIe Switches and Micron 9650 NVMe SSDs leverage next generation interface technology to deliver increased bandwidth, low latency, and scalable storage connectivity Industry veteran joins Liqid's executive team to lead product management, strategy, and engineering as company's software-defined memory and GPU pooling solutions gain traction Company launches employee matching and community seed funding, reinforcing Micron's broader U.S. investment and workforce development strategy Supply commitment supports next-generation GM vehicle platforms with reliable, high-performance memory and storage solutions Generating $41.6 billion, up 74.3% QoQ and up 347.3% YoY Solutions deliver high-performance, agile, and efficient GPU, memory, and storage scale-up and scale-out to support AI inference and other power-hungry, compute-intensive workloads.

AutoLabs achieves high reliability but requires expert oversight. AutoLabs leverages self-correcting AI technology to bridge the gap between scientists and engineers, ensuring accurate and reliable scientific experiments. This advancement could revolutionize laboratory automation and enhance research efficiency. Key facts. * AutoLabs achieved 100% success in hardware instruction loading for 4 out of 5 experiments, indicating high reliability. * The shift to a multi-agent architecture mitigates risks of instruction loss, enhancing operational efficiency. * Expert involvement significantly improves outcomes, revealing a competitive vulnerability for non-expert users. * Mixed results from retrieval-augmented generation highlight potential financial risks in AI integration. * Plans to expand AutoLabs' capabilities suggest strategic shifts towards comprehensive lab automation solutions. Summary. Researchers at the Pacific Northwest National Laboratory (PNNL) have introduced AutoLabs, a generative agentic AI designed to enhance laboratory automation by translating experimental goals into actionable instructions for robotic systems. Published in Scientific Reports, this innovation aims to bridge the gap between scientists and engineers, enabling more efficient and accurate laboratory processes. The significance of AutoLabs lies in its potential to streamline experimental workflows, reduce human error, and ultimately accelerate scientific discovery. AutoLabs employs a self-correction mechanism that identifies and rectifies errors before they reach the laboratory hardware. This capability is crucial, as traditional methods often require extensive collaboration between scientists and engineers, whose expertise does not always align. Gihan Panapitiya, a data scientist at PNNL and lead author of the study, emphasized the importance of using natural-language dialogue to facilitate this process. The AI's ability to conduct self-checks helps ensure that protocols are accurate and reliable, which is essential for maintaining the integrity of scientific experiments. The development of AutoLabs is part of a broader trend in laboratory automation, following similar initiatives such as Carnegie Mellon's Coscientist and other projects from institutions in Hong Kong and Canada. These advancements indicate a growing recognition of the need for AI-driven solutions in laboratory settings, where the complexity of experiments often outstrips traditional manual methods. AutoLabs distinguishes itself by achieving a 100% success rate in loading hardware instructions for simpler experiments, although it faced challenges with more complex tasks. The testing phase of AutoLabs involved multiple configurations of its agent architecture, with results indicating that while the system performed well in controlled conditions, it is still in a developmental stage. The researchers noted that the system's performance was confined to a single laboratory and instrument, suggesting that further validation across diverse settings and equipment is necessary. This limitation highlights the ongoing need for human oversight, as even expert users missed certain errors during the testing process. AutoLabs operates on a multi-agent architecture designed to maintain clarity and focus throughout the experimental process. Each specialized agent is tasked with specific functions, reducing the risk of losing track of instructions over time. This structure not only enhances the AI's operational efficiency but also allows for scalability as the system expands its capabilities to include literature review and hypothesis generation. Despite its advancements, AutoLabs still requires an expert in the loop to optimize its performance. The research revealed that while non-expert users performed worse than the AI operating independently, expert intervention significantly improved outcomes. This reliance on human expertise underscores a critical dynamic in the market: while AI can enhance efficiency, the complexity of scientific research necessitates human judgment and oversight. The implications of AutoLabs extend beyond laboratory automation. As AI technologies continue to evolve, companies in the life sciences and pharmaceuticals may find themselves increasingly reliant on these systems to drive innovation and reduce time-to-market for new products. The integration of AI in laboratory settings could lead to a paradigm shift, where human roles transition from direct execution to oversight and strategic input. Looking ahead, the evolution of AutoLabs and similar systems signals a transformative phase for laboratory automation. As these technologies become more sophisticated, organizations will need to adapt their operational frameworks to leverage AI effectively. This shift may prompt a reevaluation of workforce skills, emphasizing the importance of training personnel to collaborate with AI systems rather than compete against them. The future of laboratory research will likely hinge on this synergy between human expertise and AI capabilities, shaping the next generation of scientific inquiry. Frequently asked questions. How does AutoLabs improve the accuracy of laboratory protocols? AutoLabs utilizes a self-correction mechanism that identifies and fixes errors in protocols before they are sent to the robot. It employs guided and unguided self-checks to ensure procedural correctness and catch chemical amount errors, iterating until the protocol is error-free. What role do experts play in the AutoLabs system? Experts significantly enhance the performance of AutoLabs by providing oversight and reviewing protocols before execution. However, even experts can miss errors, indicating that human involvement is beneficial but not foolproof. What are the main features of AutoLabs' architecture? AutoLabs employs a multi-agent architecture where each sub-agent is responsible for specific tasks, such as chemical calculations and processing steps. This structure helps maintain clarity and reduces the risk of the system losing track of instructions during complex tasks. What challenges did researchers face when testing AutoLabs? The testing of AutoLabs was limited to a single lab and instrument, and while it achieved high success rates for simpler experiments, it encountered occasional issues with more complex tasks. The paper describes the results as a "case study," highlighting the need for further validation across different platforms. How does AutoLabs handle ambiguous details in protocols? AutoLabs includes prompts that ask users to confirm ambiguous details, which helps improve the accuracy of the generated protocols. This feature is part of a broader set of guardrails designed to enhance the reliability of the system before executing laboratory tasks.