careers · open role
principal investigator, biological computing
about frontier computing
frontier computing works on cultured neural tissue as a computational substrate.
The underlying observation is not new. Biological neurones in vivo perform inference at very low power, several orders of magnitude below what a comparable silicon system would draw, and the cells manufacture themselves.
What has been missing is any serious attempt to find out whether that can be turned into something reproducible, scalable and cheap enough to be useful. For most of the past decade the answer was clearly no, for well understood technical reasons. Several of those reasons have weakened: iPSC differentiation protocols have become more reliable, microfluidic culture systems now survive well past the four week mark, and reading from and writing into cultured tissue with multi-electrode arrays is better understood than it was.
We should be plain about where we are. We have learning on a very basic substrate, and a strongly supported hypothesis that further scaling of substrate size will bring further computational results, enabling energy efficient computation at scale while circumventing von Neumann architecture bottlenecks.
That is a starting point, but the significant work ahead is to establish whether the mechanisms we are seeing are reproducible, generalise to more structured circuits, and survive being scaled. Credit assignment is an incredible challenge and an open question within biological neural networks, and one that our company is actively solving.
the research programme
Three codependent problems define the work.
1. the cost of culturing substrates at scale
Large scale neuronal culture is currently expensive enough that very few groups can do sustained work with it. We think that is the binding constraint on the whole field, and that it is more tractable than it looks.
The useful denominator is cost per neuron-hour rather than cost per millilitre of media, which decomposes multiplicatively into chemistry, volume and density. Within chemistry the drivers are not where one might expect: in a representative maintenance medium the basal medium and the B-27 supplement together account for the large majority of per-millilitre cost, while the recombinant factors, which are by far the most expensive per milligram, contribute very little because they are dosed at trivial mass. Both of the dominant inputs are commodities, and commodities respond to process work rather than to chemistry.
Volume and density compound on top of chemistry, and have received less attention, largely because the protocols in general use were developed for developmental biology rather than for sustained culture. There is meaningful headroom across the product of the three.
We have made real progress here, and it is the most mature of the three programmes. The goal is not a proprietary advantage in reagent purchasing. It is to bring large scale neuronal culture within reach of ordinary experimental budgets, including our own.
2. reproducing core neuronal training mechanisms
Most work in this area imports the reinforcement learning apparatus from silicon: define a global reward, propagate it back, and hope the tissue converges. This is expensive to build and expensive to run, and it scales poorly as neuron count rises and credit assignment gets harder.
We are interested in the opposite approach, which is to let the biology do as much of the learning as it already knows how to do. Local mechanisms, spike-timing dependent plasticity and neuromodulator-gated plasticity among them, are implemented natively by the tissue at no cost to us. If a training environment can be designed so that the desired change is what those mechanisms produce anyway, the control apparatus becomes much simpler and the approach scales with neuron count rather than with the complexity of the reward circuitry.
Our current work is in cortical computation, and the near-term direction is to extend it toward hippocampal circuit work, where the relationship between architecture, plasticity and function is better characterised and where there is a substantial literature to argue with. The immediate scientific question is whether the learning we see on a simple substrate reproduces in a structured one.
3. building out mature neuronal cultures at scale
Culture size is limited by oxygen and nutrient diffusion, and beyond a few millimetres the core of a construct necroses. This is a well known constraint and it is the proximate reason nobody has built a biological system with the connected mass that would make any of this interesting.
Maturation is the second half of the problem and is often treated separately, which we think is a mistake. A large culture of immature neurons is not useful; the target is tissue that is both sizeable and functionally mature, held in that state for the duration of a workload rather than for the duration of an experiment.
Vascularisation is the approach most of the literature takes, and progress there is real. We are interested in whether it is the only approach, and our roadmap is not contingent on it. We would be glad to be argued with on this point by someone who knows the perfusion literature better than we do.
about the role
This is the founding scientific leadership position at frontier. We have called it Principal Investigator in this job posting rather than Chief Scientific Officer because the substance of the job is closer to running a research group than to running a corporate function: you would set the scientific agenda, choose the problems, build the team, and be accountable for whether the science works. The title officially will be CSO, but your functional work will have much more freedom than this role traditionally implies, given the greenfield nature of the field.
However, your work will fundamentally be application-oriented, designed to push the capacity for biological substrate approaches. This is not a role performing blue-skies research, and the programme will have to be justified towards our business goals. First and foremost, we are looking for someone who understands these tradeoffs, and appreciates the science they are pursuing must benefit our commercialisation strategy: building larger tissues with both greater and denser computational and memory capacities.
You would be the first senior scientific hire, and as part of that role you will define the future composition of the team alongside the CEO, and help buildout a scientific programme that drives substrate progress and feeds into our commercialisation efforts.
what you would be doing
Research leadership. Own the scientific agenda across the three programmes above. Decide what is attacked, in what order, with what resources, and decide what is stopped. Design the experimental programme that takes us from the substrate we have now to something considerably larger, with the network kept in the loop rather than distilled away.
Building the group. Recruit and lead the founding scientific team across wet lab, instrumentation and the computational side of the training work. You would have real latitude over who joins and how the group is organised. If you hold views about how research groups ought to be run, this is an unusual opportunity to act on them.
Technical judgement. Decisions about tissue configuration, media formulation, electrode interface and training environment interact, and have to be made as a set rather than separately. This is the part of the job that cannot be delegated and is the reason the role exists.
Scientific representation. Publish, present, and represent the work to the field, to collaborators, and to the people who need to understand why this is a long horizon programme rather than a short one. Much of the work that you are leading will be obfuscated within the short term, while we scale the platform and create a technical moat around our outputs, but it's vital that we share the fruits of our work with the scientific community as much as can be reasonably permitted.
what we are looking for
A PhD in neuroscience, bioengineering, tissue engineering, biophysics, electrical engineering or a related field, together with a record of independent work: postdoctoral, fellowship-funded, group leader or industrial. We are interested in what you have built and what you understand rather than in how long you have been doing it.
Substantial hands-on expertise in at least two of the following, and the ability to reason about the whole system:
- Neural cell culture. iPSC differentiation to cortical or other neuronal identities, long duration culture, media formulation, and the practical business of keeping tissue alive and functional over months.
- In-vitro electrophysiology. High density and CMOS microelectrode arrays, closed-loop stimulation and recording, spike sorting and population analysis at high channel counts.
- Tissue engineering and microfluidics. Three dimensional culture, perfusion, scaffold and substrate design, and a working understanding of designing culture substrates to circumvent the mass transport limits that govern construct size.
- Computational modelling of neural systems. Spiking network models, plasticity rules, and the analysis of learning in biological networks.
We would particularly like to hear from you if you have:
- Worked on plasticity or learning in cultured or ex-vivo tissue, especially with local rules rather than externally imposed reward. Experience within biocomputing on neural organoids and eliciting learning in these tissues is particularly beneficial.
- A background in hippocampal circuit physiology, or in the relationship between neuronal circuit architecture and memory more generally.
- Taken a biological protocol from research grade to something reproducible and cost controlled, through process development, QC, automation or in-house reagent preparation.
- Worked on maturation or on the size limit in three dimensional neural constructs, and formed a view about it.
- Built instrumentation across electrophysiology, optogenetics, or other related areas, as well as used it.
A note on temperament, since it matters here as much as technique. This work suits people who are comfortable being among the few in the world attacking a particular problem, who will stop their own project when the data says to, and who are willing to treat a cost model as a scientific object rather than an administrative chore. It suits people who are not troubled by a thin literature. It is a poor fit for someone who needs an established field to orient within, and we would rather establish that early than late.
We are not looking for someone whose work primarily applies existing organoid culture or MEA tools as instruments. The contribution includes the design of the system itself, recognising the importance of co-development of the technology substrate with the training approach itself.
what we can offer
Compensation. £100,000 to £180,000 (approximately US$136,000 to US$246,000 at current rates) depending on experience, plus 1 to 4% equity on a standard four year vest with a one year cliff. The range is genuine and negotiable, and it reflects that this is a founding position. You would be an owner rather than a member of staff.
Scientific authority. You choose the problems and you stop the projects. The founder's job is to make sure the company survives long enough for your programme to run. Yours is to make sure it is the right programme.
Resources. Recurring biology, wet lab and microfluidics infrastructure are funded, and you would shape what we buy rather than inherit someone else's equipment. Budget for the founding scientific team is yours to allocate. You will not be writing grant applications.
A decision, promptly. We will give you an answer within two weeks of a first substantive conversation, and we will tell you the reasoning either way.
Visa sponsorship. We can sponsor. Candidates at this level frequently qualify for the UK Global Talent visa, which is quicker and not tied to an employer, and we will support either route and cover the costs.
The right to publish. You will publish. Where something is commercially sensitive we will say so and agree a timeline with you in advance. Protecting your scientific standing is a condition of this working, not a concession we are making. With regards that, much of the biocomputing work will likely be limited to being published after a 2 year period, particularly regarding core IP that generates value for the company. We will however ensure that you have time to work on problems that lean towards basic science and will be significant contributions within neuroscience, and you will be resourced financially and empowered in hiring decisions to be able to do this.
Location. UK based, in the lab. This is wet work and cannot be done remotely. We are open to discussing relocation, but your role will require you to be physically located in Cambridge with the rest of the team that we will be hiring.
please read this before applying
frontier computing has raised $10M+ to date, and is actively raising more. That provides genuine runway and a substantial equipment budget, which in practice means the research programme you design is one you would get to run rather than one you spend a year fundraising against.
The team at present is the founder, Michael Domarkas: Cambridge physicist, four years in neuromorphic computing and two of those specifically on organoid computation, and has been grant funded to work on this previously. You would be joining a funded company with a small number of serious competitors worldwide and almost no organisational inheritance. Depending on your temperament it is either the most appealing or the most uncomfortable thing about the position.
We have tried to describe the state of the science accurately rather than favourably. If the risk profile is wrong for you at this point in your career, we would rather know now than half way through your first year in the company. We'd still be interested in chatting with you, particularly around roles that might become relevant in the future.
how to apply
Please send the following to hiring@frontier.london, with "PI application, [your surname]" in the subject line.
- A CV, in whatever format you prefer. Include publications that are published and accessible, with links; nothing in preparation. Include funding you have held, making clear what was awarded to you personally as against a consortium, and anyone you have supervised.
- A research statement, maximum three pages. Describe the three pieces of work you consider most important, with links, and what they established. Then describe what you would do here: which of the three problems you would take first, what you would need, and what evidence within twelve months would tell you that you were wrong.
- A one page letter on why this rather than the alternatives available to you. We want to hear what excites you about doing science with us, and what motivates you in our research and engineering mission.
- Thoughts on team, any postdocs that you would want to bring on to work with you, and how you think about working with a team that is diverse across age and technical backgrounds, understanding that significant portions of the team buildout will be under our engineering roles to empower scaling the work you will have helped develop.
We do not need a teaching statement. In line with DORA, we are not interested in citation counts, h-indices or journal impact factors, and will disregard them. We will read the papers!
apply — hiring@frontier.london
Informal enquiries before applying are genuinely welcome. Write to michael@frontier.london and we will find a time. If you are still deciding whether to leave academia at all, that is a reasonable conversation to have with us even if this particular role turns out not to be the answer.
Applications are reviewed as they arrive, and we will close the position when we find the right person.
Frontier Computing LTD is an equal opportunity employer. We are recruiting from the systems neuroscience, tissue engineering and computational communities, and we welcome applications from every background. If you need any adjustments to the hiring process, please inform us within your application and we will be happy to accommodate to the best of our ability.